Dell Technologies Inc. (DELL) Earnings Call Transcript & Summary

Please welcome Dell Technologies VP of Investor Relations, Paul Frantz.

Hello, and thanks to everyone for joining us today for our 2025 Dell Technologies Securities Analyst Meeting. You'll find our press release from today, our presentation and related disclosures as well as additional content and information available on our IR website. Before we get started, I'd like to share with you our Regulation G and safe harbor disclosures. During this meeting, unless otherwise specified, all references to financial measures refer to non-GAAP financial measures, including non-GAAP revenue, operating income, net income, adjusted free cash flow and earnings per share. A reconciliation of these measures to the most directly comparable GAAP measures can be found in our meeting materials and SEC filings. Growth percentages unless otherwise specified, refer to year-over-year changes. In addition, statements made during this meeting that relate to future events are forward-looking statements based on current expectations. Actual results and events could differ materially from those rejected due to a number of risks and uncertainties, which are discussed again in our materials and SEC filings. We assume no obligation to update those forward-looking statements. Now turning to the agenda. We'll begin with presentations from Michael, Jeff, Arthur and David. We'll take a short break, reconvene for Q&A, take another break and then host the management reception for everyone that's here. With that, let's turn it over to Michael.

Good morning, everyone, and thank you for joining us. It's been 2 years since we hosted our last Analyst Day, and we've been busy at Dell Technologies. During that last 2-year period, the pace of change has been unprecedented, and the pace of our innovation has accelerated to match that. From the AI PC that you can hold in your hand, to the galactic scale implementations, we're building the technology-driven future. And our engineering, our supply chain, our customer relationships and our services set us apart. As AI continues to expand into businesses and governments around the world, the opportunity ahead for us is massive. And customers are hungry to understand AI and they need our help to deploy intelligence at scale. We're successfully translating that demand into growth and strong cash flow that we've largely returned to our shareholders continuing our 4-decade journey of value creation. AI technology is now driving 45% of U.S. GDP growth, and we believe this is just the beginning. When a customer can realize productivity gains of 10% or 20%, it's interesting, but with sightings of 30% or 40%, it becomes an absolute competitive imperative. And with a $114 trillion global economy, 2/3 of which is services and knowledge based and with the kind of productivity gains that we're talking about, AI is projected to generate an additional $15 trillion to the global economy by 2030, leading it to $150 trillion global GDP. At the core of all this growth and opportunity is data. And with the overwhelming majority of the world's data created in the data center or in the physical world at the edge, it's increasing at compounding massive and accelerating amounts. And that proprietary data is the fuel for our AI factories. Data goes in and competitive advantage comes out. For our customers, it's almost that simple. But beneath the surface and beneath that simplicity are incredibly complex engineered solutions. The infrastructure that generates tokens and creates intelligence. Hardware is cool again, and we are uniquely positioned, providing opportunities to grow across both data center infrastructure and AI -- and AI PCs. Now for 50 years, technology was all about calculating and computing. But now we're evolving into machines that help us think and are thinking for us. And what are these models creating? They're creating intelligence. How big is the market for intelligence? It's very big. It's probably the biggest market that was ever created. Even with all the innovation that we've seen over the past 2 years, we are still in the early stages of AI's S-curve adoption. And the models that we have today, while they are impressive, they're the worst they'll ever be, and each wave of innovation builds on the next. One-shot LLMs led to reasoning models and multimodal models. Agents combine understanding, decision-making and action, and multi-agent systems collaborate, negotiate and coordinate to get complex work done. And the great thing about all of this is that the world is going to need a whole lot more compute and data storage and networking, which is exactly what we do here at Dell Technologies. So in nearly every conversation with customers, AI is the central topic. Decision makers want to know how AI can drive competitive advantage, efficiency, faster product development, innovation and growth. And leading enterprises are already seeing strong ROI, treating IT spending as an enabler rather than a constraint. And the other 90%, I would say, are still figuring it out, which is a massive opportunity for Dell. The momentum is clear. 85% of enterprises plan to move GenAI on-prem within the next 24 months. And we're engaged from the very start of the journey. Before infrastructure, the heavy lifting is organizing all of their data and choosing the right models for each use case. And customers want to learn from Dell's own modernization and how we apply AI for competitive advantage in our business. And when they're ready to scale with an AI factory, Dell is already in that conversation, with a broad portfolio spanning data center to edge across all industries and company sizes. And we've already engaged with over 3,000 enterprise customers, and that's just a start. Now over the past several years, we committed to our long-term value creation framework, and we delivered against that. And we've roughly doubled earnings per share over the last 5 years. Earnings per share is scaling, growing faster than revenues. And we've returned $14.5 billion to our shareholders, 97% of adjusted free cash flow has been returned since the inception of our capital return program. And going forward, we're strengthening our long-term value creation model with more growth and a much higher EPS target, 15% plus, to double EPS again over the next 5 years with a continued commitment to shareholder return. And David is going to walk us through the details. And now let's turn the stage over to Jeff for more details.

Has it really been 2 years? I don't know, for me, it feels like 40 days ago, our last earnings call. But time flies. And these days, so does the pace of change. Michael just made a case for that. And what I'd like to do for the next few minutes is to talk about that pace of change, how big it is, the speed of which it's coming and ultimately the opportunity that it presents for us at Dell Technologies. Michael said it, but I want to be very clear. This pace of change is fundamentally changing our company. It's changing the way we innovate. It's driving our innovation. It's driving growth. It's driving value to our stakeholders. And quite honestly, it's playing right into our hands right where our strategy is. And as I talk about our strategy and operating model, I'll link this growth opportunity with the 4 decades of foundation of our company and why they 2 intersect and present this opportunity. So since we last met, things have gotten a little crazy. Would you agree? We can't have a conversation at all where AI isn't part of the topic. AI, AI, AI. That growth, that topic has accelerated. And what once felt exponential feels much more like factorial growth, bringing new opportunities to innovate, new opportunities to serve our customers literally daily. I'm going to give you a few examples to help illustrate that case. The first being in the area of AI investment. 2 years ago, we stood in front of you, with all of the best knowledge that we had about our industry and we said, "By 2025, there'd be $200 billion of AI CapEx spend." It's going to be over $400 billion this year. In AI hardware and services, we showed a forecast that by 2027, there would be $124 billion of AI spend in those categories. It's now expected to exceed over $310 billion. The data center where the rubber hits the road, where all of that spend shows up, 2 years ago, we thought in the United States, the data centers would require 245 trillion terawatt hours of power by 2028. That number is now nearly doubled at 450 trillion kilowatts per hour. What's driving this? It's inference. The demand for inference, long thinking, auto aggressive reasoning models is now requiring more computational intensity. At minimum, at a minimum, 100x, 2 orders of magnitude greater than we thought less than a year ago. More than 2 orders of magnitude more than we thought just a year ago. And while that shows up in, in the form of tokens, the measure and what a tokens need, tokens computational capacity and capability to provide them. We thought as we model this, that inference would drive by 2028, 1 quadrillion, that's 15 zeros, 1 quadrillion tokens. Now it's 57 quadrillion, and I'm sure we're wrong. And enterprises are adopting this at an incredible level, and it's much beyond tech. It's about reasoning across data, it's how they power their customer service models, it's automating IT workflows, it's summarizing research. And all of that work consumes tokens and tokens translate quite simply into computational need. More AI driving more tokens, driving more infrastructure, more AI, which is why we continue to talk about the accelerating pace of AI. And Michael touched on this earlier, but I think it's important as well, the models themselves are getting better. The progress and accessibility of today's model versus where they were 2 years ago is vastly different. In 2023, we largely talked about one large language model. Today, you can run an open-weight model on consumer hardware that outperformed that model of 2 years ago. And today, the state-of-the-art is 98% cheaper and more capable than what we thought was state-of-the-art as recent as past 2 years ago. It's an extraordinary shift, and we like to say we haven't seen anything yet because it's true. The rate of which the capability is growing or what computational intensity is providing is only accelerating. And it's just not large language models. There's been an explosion in the category of small language models. Today, we're tracking more than 50 performance small language models in our industry. And that's critical, and I think it's key because it runs on environments like AI PCs that reduce cost, latency power consumption while still providing incredible capabilities around coding, task automation, IT chatbot assistance, content creation and so much more. This evolution makes AI more accessible today than it was 2 years ago, more accessible than ever. It's making life better. It's raising human cognition, all doing are leading to us as humans doing less of the mundane work and more of the exciting value creation work, the creative work long term. And enterprise companies, are using AI today, using it today to improve or drive fragmented data sources, automate customer service, optimize their supply chains, do fraud detection, detect anomalies in their IT systems, even accelerate in a practical implementation drug discovery. In other words, they're using AI to unlock competitive advantage for their specific enterprises. And these changes, I would argue played to our favor. It's a nice tailwind for growth and value creation. We've just inferencing plays a much larger role. It just keeps climbing as these deep reasoning models and agents proliferate. And again, we've not seen anything yet. We're just at the very early, Michael showed it on his S-curve, just beginning to see the capabilities that are here. And all of this drives more need for compute capacity, to drive better reasoning cycles, to drive better outcomes, to drive better prediction of your next best action, to drive better pattern recognition and drive at the end of the day, better decision-making. More insights, better decisions faster. That's the outcome and why we believe this is disruptive and every company will have to deploy this capability to be competitive in the future. And our engineers are working on the technology at the largest at-scale clusters that do this. Enterprises are actively looking for a trusted partner like us to help them get AI adopted quickly. I spent a lot of time with customers. They ask a very similar pattern of questions. Where do I start? Is my data AI able? Do I have the space? Do I have the power? Am I actually going to see a return on this investment? Interestingly, only 1% of leaders and companies today think they're mature on the spectrum of deploying GenAI. But yet 87%, 1% think they're mature, 87% of them think they're going to see AI drive revenues in their company over the next 3 years. 1% mature, almost all of them believe that GenAI is a source of revenue growth for their companies in as little as 3 years. Our answer is the Dell AI Factory. It helps support customers at every step of their AI journey. It is really the playbook of how to deploy AI at scale. And Michael talked about this notion of exponential data growth. 80% of that data will be unstructured, not text. Think about it as video, music, multimodal, all types of rich content, rich data, mostly derived at the edge, coming at companies that have to do something with it. And to support those unstructured data at scale and the speed of which you're going to -- what it's going to come at you, you need a real AI optimized storage and networking portfolio, one of which we have the leading position in the marketplace. And those AI workloads, when you look at the storage architecture need to run on a disaggregated storage architecture. Why? For performance and scale. 83% of customers -- or excuse me, let me say it differently. Customers seeing 83% faster read throughput of their AI data lakes and data with disaggregated storage architectures. So fundamentally, the storage architecture has to change to feed the beast, to feed these computational engines and the way to do that is with a disaggregated architecture. Arthur, I'm sure we'll talk about this in a bit. But when you think about Dell AI data platform, that is a necessary foundation for customers to actually take advantage of the technology, and we think we had a game-changing opportunity here in the storage area to help our customers with AI, data and a storage architecture that delivers that. And then lastly, I'd be remiss if I didn't talk about these models running locally on PCs, improving the latency for time-sensitive tasks, setting network bandwidth, allowing you to operate disconnected in many ways. But the punchline, the good old PC continues to be a great productivity device even in the era of AI and is actually essential for doing AI at the edge. It's not going anywhere. And we've positioned ourselves from our PC portfolio all the way to the galactic mega clusters Michael talked about earlier, that spectrum is what we do. We've positioned ourselves for the opportunity and we're in it to win it. And you're going to hear that consistently throughout the presentation. So let me switch gears a little bit and talk about why we're uniquely positioned. As an engineer, maybe this won't surprise you, but I'll start with why we think we are in a position to win and go after this opportunity is our engineering expertise. We've been building large-scale systems for many, many decades, deploying in data centers for many, many decades. This stuff is hard to do. The engineering step function to deliver these at scale is significant. And we're building these specialized custom solutions of tens of thousands of GPUs for the biggest names in our industry, xAI, CoreWeave, ServiceNow, G42, Mistral to name a few. We're designing these clusters of over 100,000 GPUs that scale exponentially. And we're not working off a reference design. We're not working off of customers building materials. We're engineering bespoke, optimized solutions that solve what customers care most about, performance per dollar and performance per watt. That does not come from a reference design, that does not come from bill of materials. That comes from hardcore engineering understanding what the customer problem set is, the environment we're working in and the delivering and optimized solution for them. And what it really means if we translate in simple terms, we're optimizing for their data center. It's more than the node, it's more than the rack, a row of racks, it's for the data center. And it's beyond the things that you would expect of compute, networking and storage, it's power management, it's cooling, it's software optimization, software management. And these investments are -- or these are areas that we've invested in now for many years that we're paying the dividends from and seeing it as a key to our differentiation in the marketplace. One of the engineering concepts we've used in this area is what we call an engineering pod. We've created these engineering pods, invested in the engineering capability to assign a pod to our largest Tier 2 CSPs to our sovereign and enterprise customers, working directly with them on their needs and it allows us to extend the utility of how we apply our engineering skills to each and every customer opportunity. So it's beyond compute, beyond network and beyond storage. It's really engineers that do thermal design, power management design, data center engineers to help deploy these dense fabrics and dense infrastructure in very small spaces to optimize for performance per watt and performance per dollar. These engagements, as you might imagine, with these very, very large customers are heavily engineering-led. We work with our customers on their next tranche of deployment. We take what we get from each of those interactions and move through the entire customer base in the entire product design. We go through multiple designs, multiple iterations in a very short period of time. The idea is you go from the initial concept to a design to delivery in very short order. That process does take time, but we've compressed that in what I hope to show you or talk about it in a few seconds in a very, very small time capsule, which is differentiated for us, and allows us to take that deep engineering expertise and to rapidly deploy our products faster than anybody in the marketplace today. One of the reasons we win is what we call rapid scale deployment. Our execution time to deployment and installation is differentiated. These customers have their own backlog, they need to convert to revenue and time to market is essential or as competitive to them. And if you look at what we've done over the past 2 years, I think it's quite impressive. First to market with the GB200, 2 to 6 months ahead of our competition. First to market earlier this year with the GB300. We put 110,000 GPUs, a liquid cool infrastructure in a data center that was operational in weeks. That's 27,500 nodes, 1,536 racks and over 6,000 switches. All up and very short or a handful of 6 weeks, driving tens of trillions of tokens when it's up and operational and has laid the foundation for this, which is the best in the industry. When we deliver a GB200 rack or a GB300 rack to our customer, it's up and operational in the data center and up in 24 to 36 hours. From our dock to our customer, installed in their side 24 to 36 hours. We believe it is a huge differentiator, and it's why we're winning in the marketplace today. And then you couple that, which allows us to be able to meet those time frames with our deployment and installation services, we're unmatched. It's why you're seeing the momentum in our business to date. Our deployment, when customers think about our services, they do deployment installation. But beyond that, we're with them every step of their service journey. Comprehensive support deeply engaged throughout the full life cycle, maximizing what they care most about is uptime. Uptime of these very large capital investments is key, and we believe we've cracked the code. And in some of these largest customers in these very, very large customers, we've seen uptimes of our portion of the deployment north of 99%. At scale design done in a short period of time, delivered very quickly once off the truck, installed and working, uptime north of 99%. Then we wrap around that a partner ecosystem that is unmatched and deeply rooted in customer choice, whether that's NVIDIA, AMD, Intel, Hugging Face, Meta, Google GDC with Gemini, OpenAI, xAI, Cohere, Red Hat, Glean to name a few. These partnerships are a collaborative effort that allow us to ultimately make the technology for our customers, easier to deploy and happen fast. Speed and easy. That's our goal. And then we round out that capability with the slide that you see in front of you with the financing capability that we have. Our bank allows us to have flexible competitive financing offerings for our customers. Together, we believe these capabilities give us a competitive advantage in the marketplace. We see it with the providers across the world and an it extents to even a broader set of customers. I think about customers, we generally think of them in 3 categories. They're on the page here in front of you, enterprises, sovereigns and Tier 2 CSPs. And we're using the knowledge that we gained from our Tier 2 CSPs and it trickles down. Those at-scale designs at speed and those customers coming back to us tranche after tranche allows us to take that information and for us to continue to tweak and improve our designs. And it trickles down to the other 2 categories of customers, our sovereign customers and our enterprise customers. And many of those enterprises are really thinking about how to deploy this, as I mentioned earlier, fast. You think about sovereigns, they're very much like our Tier 2 CSPs, very large at-scale deployment, similar technical needs. We're still in the early days of the sovereign opportunity. We have several wins that we're very proud of. You probably heard us talk about it, but one of them is with the United States government, the Department of Energy, the NERSC-10 supercomputer with G42, Nscale to name a few, many more on our pipeline. And then that trickles down to our enterprise customers, which is our bread and butter. We've been serving enterprises for over 4 decades. Michael talked about the number. It's a number that we've publicly spoke about and continue to reinforce of the enterprise opportunity over 3,000 Dell AI factories to enterprise customers today, and it's growing. Robust portfolio. Many of our customers are in this piloting and testing phase. They're moving to production. I mentioned examples of how they're moving to inference. But as I described, 1% are in the mature category, there's so much more to do in enterprise. The opportunity is immense. And there are many that haven't started. And quite frankly, if they haven't started soon, they're going to fall behind and become uncompetitive in their sectors. So the opportunity for us to accelerate that, expose the Dell AI Factory to more of those enterprise customers to help them deploy AI quicker and make it easy for them. We have a solution for every vertical, for every form factor, on-prem, at the edge with an ecosystem to support it. I'm going to go through a few products to give you a sense of the breadth of that. But the first will be the PowerEdge XE9780 with Blackwell B300 GPUs that run inferencing 11x faster, direct liquid cooling up to 256 GPUs per our rack, the PowerEdge 7725, the XE 7740, the XE 7745 with the RTX Pro 6000, which essentially think of this as a great enterprise offer. It is air cooled PCIe option at a value price, a very attractive price point. You take that with our storage portfolio and the AI data platform, or you take our fast scalable file and object assets, PowerScale, ObjectScale with its leading density performance, efficiency and manageability. You add on top of that Project Lightning, which we talked about earlier last year, around a parallel file system, you had Project Dynamo to it, which basically puts KV cashing for inferencing. And then you add our Dell Data Lakehouse and you have a streamlined way to ingest data. That portfolio fundamentally is differentiated in the marketplace for enterprises, and it gives us an opportunity to attach more around it, networking and more of our storage assets. And for us, those 3,000 customers that I talked about, they're seeing real returns on their investments, their use cases, and they're coming back and buying again. So not a onetime pilot see, real return on investment, more use cases coming back to buy more AI, and we're seeing great traction and a great start there. Shifting gears a little bit. The engineering curve is getting harder. So as much as I think engineering differentiates us, I think it continues to differentiate us because the design requirements are becoming more steep. If I put this in the context of the demands for the Dell AI Factory design are evolving at breakneck speeds because the technology coming at us is driving us to do that, whether that's power, density, cooling, it's now front and center, the architectural choices, the architectural innovation is not slowing. In fact, it's accelerating. There are more opportunities for us to innovate given the picture that you see in front of you than ever before. It wasn't too long ago that the standard data center was roughly 10 to 12 kilowatts of power. Our first rack scale designs were 120 kilowatts of power, today, over 200 kilowatts of power per rack, on a path to 500, on a path to 1 megawatt. If you look across the portfolio of technologies, I'll pick one here, for example, the silicon road map of NVIDIA, we go from Hopper to Blackwell to Rubin to Feynman is this type of power and density in front of us. So more power per rack, more GPUs per rack, driving more innovation and opportunity for us. And we are staying ahead of designing what's next and building what we believe is essential and key for our customers. How do we build the densest, most power-efficient clusters, maximizing every inch of data center space given and providing that in an optimized fast way. The biggest challenges, cooling, power and software. I'll walk through those real quickly. On the cooling side, we have to get the energy density off the GPU and out the rack. We're developing a complete cooling system from the ground up. We're looking at new materials for better thermal heat transfer, so the thermal interface from the chip to the heat sink and out being able to get that energy density out. We're designing cold plates. We're designing smart manifolds, leak sensors, and rack cooling units, all our designs, all our IP, all are an opportunity for us to differentiate. We're working with the power manufacturers to explore new materials like gallium nitride, silicon carbide to improve power density. We're designing a new power shelf to meet the demands of what you saw again in front of you here of all of that power in each one of these GPUs. And for software, it may seem trivial that oh, software management and updates ought to be easy, but there are many, many components in a rack, much less rows of racks of software that have to be optimized on a weekly basis and in some cases, daily basis. And doing that across a cluster of 100,000 GPUs is a pretty significant challenge, testing the orchestration to ensure flawless execution and not have downtime. Remember, 99% uptime cannot be impacted by updating things, which really takes me back to one of the core tenets of why we win services. It takes a lot of services to deploy and install. And I think going forward, as AI evolves, services will play an even more important role. So if I summarize that and why I think services matters and what we're doing from an engineering side and staying ahead of the curve here, we're doing what we always do. We're managing the complexity. We're staying ahead of the curve. We're designing for the innovation that's coming, and we're doing that at record speed, something I've never seen us do at this speed in my near 4 decades at the company. So as we continue to execute aggressively and we look at our operating model and our strategy, Michael showed it, we talked about this. Nothing's changed. What differentiates us our leading end-to-end solutions, our industry-leading go-to-market model, our supply chain and services, the 4 tenets of our operating model remain unchanged and the strategy of the company remains unchanged. These are what makes us who we are. This is what makes us differentiated in the marketplace. And quite frankly, it's where the investments go. It's how we're differentiating and building new capabilities across the company. So what I thought I would do is maybe a little bit of a teaser. You've heard us on some of the earnings call talk about how we're applying AI in the company, how we're taking that operating framework that I just showed you and making it stronger and giving you some insight of a few ideas, more than ideas, things that we are doing in the company that many other companies can do the same. But we started in a place 3-plus years ago, what are we really doing here? And we found we were doing everything. And we had an environment that if you called it AI, it got supported even though it wasn't AI. And we cleaned all of that up. We found 900 projects, most of them not AI. We found that we didn't have a well-articulated strategy. We didn't have an infrastructure that could scale, and we had a data environment that wasn't ideal for AI. So we fixed all of that. We put an AI strategy together. We built a data mesh across the company. We put in the infrastructure. And once we have that foundation, we are able to address the direct needs. What were the enterprise use cases? And how would we use those enterprise use cases in the 4 areas that differentiate our company. You've heard me talk about this on earnings call, but the 4 use case -- the 6 use cases generally used in enterprises today are content creation and management, support assistance, natural language search, design and data creation, code generation and content or document automation. Amongst that, we picked 4, and we applied those to the 4 areas, and I'll give you a quick drive by what we've done. So in R&D, we've taken the idea of coding and knowledge assistance, and we're accelerating and improving our product development cycles. We have become more productive. We've reduced cycle time. We can do more work, provide more features in shorter periods of time. I talked about this at DTW, but we've implemented a service assistant. We call it next best action, the ability to have a little assistant on every service agent in our company to help them navigate a customer's challenges. The result has been improved customer satisfaction. The result has been increased efficiency and productivity in our service organization. Today, I've talked about this as well on our earnings calls, we're using predictive systems and digital twins in our supply chain to provide a more resilient and more responsive supply chain particularly in this day and era of a changing environment and changing sometimes daily. And in sales, a very exciting area for us, we've actually taken a sales chat assistant to improve seller productivity. And what we've done is we've taken all of the company's internal information. We've linked it to the external market data that we have, and we're providing our sales force with fast, accurate answers at their fingertips. They can get product insights, they can get product intelligence, they can get other customer wins. They can generate content. They can write a proposal to a customer. We can intersect the customer wherever they are in their customer journey, all across one system, one interface that works nearly instantly and providing that to each and every one of our tens of thousands of sales makers. Inside the company, it's like magic. It's the most modern sales tool we've ever given our sales force, and they're utilizing it. So as I mentioned, we've been acting as customer zero for AI implementation. We are seeing real returns on our investments. We're driving that efficiencies. You see it as we communicated in the bottom line performance of the company. We share this journey and share our knowledge with many, many, many customers. That insight helps them. We're providing the leading infrastructure solutions to help them deploy once they made the decisions, AI to get to their competitive advantages faster. And quite frankly, we're just getting started. So maybe a few closing thoughts. If Michael and I haven't done anything other than the rate at which our industry is changing is unprecedented, and we've not seen in our 4-plus decades. It's truly remarkable, and we see no signs of it slowing down. Our strategy is unchanged. We will continue to execute our unique operating model that we've built and fine-tuned over the past 4-plus decades. We're not -- hopefully, I've communicated we're not just keeping up with the industry demands. We're actually driving the industry, shaping the future of AI infrastructure. I believe we are built for this moment. The trends are working in our favor. We're excited how things are shaping up. We're all in it, in it to win it. And with that, I'll turn it over to Arthur to talk about ISG.

Good morning. It's great to be back in New York City. I hope everybody is doing well. As Jeff has talked about, and Michael, the technology industry is driving groundbreaking innovation as we usher in the transformative era of artificial intelligence, and Dell Technologies is engineering and scaling the infrastructure to make that happen. The opportunity ahead of us is extraordinary. Estimates of AI spend continue to rise. Over the next 3 years, more data will be created than in all of preceding human history. And by 2030, data centers around the world will require nearly $7 trillion in investment just to keep pace with the torrid demand of compute. These forces will combine to create a significant need for infrastructure and services that can turn data into intelligence, complexity into clarity and to do so, securely, efficiently and at scale. The rapid developments that we see in artificial intelligence will also disrupt traditional data center architectures, disaggregated will rain, silos will disappear, data will flow seamlessly. And what was once known as dark and cold data will become observable and active constantly in circulation feeding, AI engines and agents. Our role as a trusted adviser has never been more important, and we are uniquely positioned to guide the architectures of the future. With our winning portfolio of compute, network and storage, we are empowering customers to deploy artificial intelligence where it matters most, close to their data, whether on-prem, at the edge or in the cloud, and we begin from a position of great strength. Our share leadership position, coupled with world-class capabilities in our supply chain, in our services organization, our go-to-market engine, position us well to continue to capture share, grow margin and win the next wave of AI. Over the last several years, ISG has delivered durable, consistent performance. Since FY '18, ISG has grown revenue at a CAGR of nearly 8%, operating income at a CAGR of 9%, and we've expanded operating margins 140 basis points. We are #1 in compute and storage with share positions greater than our next 2 competitors combined. In compute, we have led in revenue share for 33 consecutive quarters. Over the last decade, we've gained over 700 basis points of share. And if you exclude China, we've gained 1,767 basis points of share. Over that same period, Dell has captured 50% of the market growth in compute, more than the next 3 competitors combined. In data storage, we have been a revenue leader for 94 consecutive quarters. We are #1 in every major category, external RAID, entry, mid-range, high-end and purpose-built array. And in Q2 of calendar '25, we were not just #1 in all-flash. We grew north of 25% of very strong premium to the market, gaining 244 basis points of share. And on top of all of this, we grew a new business, our AI optimized portfolio to at least $20 billion in just 2 years. We now service over 3,000 enterprise customers and many of the largest and most relevant Tier 2 cloud service providers. Given our track record as a structural share gainer and with AI as a significant tailwind, we are once again raising the ISG long-term growth framework, revenue CAGR from a range of 6% to 8% to 11% to 14%. Our durable and consistent performance is ensured by a singular strategic focus on customer-centric innovation in a first-to-market mindset in everything that we do. Last year, we were the first to ship an NVL72 GB200 rack. That was no small feat. And still, we repeated it again this year as the first to ship an NVL72 GB300 rack. We introduced PowerCool, the most technologically advanced cooling solution in the marketplace that includes custom design and engineered cold plates, manifolds, cooling distribution units and includes rear door heat exchangers, all under single pane of glass for simplified management. In addition, we've increased the mix of our software developers that are focused on product delivery by 18%, ensuring faster innovation for our customers. We've also united the entire software development apparatus under a single agile model and a common CI/CD motion increasing feature velocity, increasing quality, all augmented with AI tools for accelerated development and expanded functionality. With these changes, we are moving extremely fast, delivering features to customers on a quarterly basis. In the areas of the portfolio where we're most advanced, we've seen velocity increase upwards of 45% year-over-year, and we expect to see similar results across the broader portfolio as we mature. Given our financial strength, our share position, our operational excellence and this customer-centric focus on innovation, Dell is well positioned to extend its leadership. And let's begin with compute where PowerEdge is the undisputed backbone of enterprise IT. For over 17 generations, Dell Technologies has led the way in compute innovation, and our latest generation of -- our 17th generation of servers is again redefining what's possible. This is our most dense, power efficient, secure, performance generation ever, designed to meet and exceed the rapidly evolving demands of enterprise customers. PowerEdge is engineered for the dual reality of IT balancing the performance and efficiency of traditional workloads while delivering the acceleration and scalability needed for AI and data intense workloads. So whether you're talking about core business applications or inferencing against scale, PowerEdge is a compute platform that is making that happen. Our servers are equipped with intrinsic security, advanced automation and cutting-edge liquid cooling technology giving customers the ability to lower their TCO, consolidate legacy infrastructure and reduce enviromental impact, consolidating workloads and preparing for the exponential growth in compute. The opportunity for transformation is significant. More than 70% of our installed base resides on servers that are 14th generation and older. This is not just a statistic. This is a clarion call for action. Refreshing to 17th generation servers allows customers to reduce power floor space bandwidth, consolidate workloads and prepare for AI adoption. And for customers who are ready for AI, as Jeff talked about, our portfolio is built for performance and scale, 14x faster training of large language models, 11x more compute for accelerated inference and direct-to-chip liquid cooling for optimal efficiency even at scale. In short, no matter how you look at it, PowerEdge is the compute foundation of the data era. And as AI elevates data as a key differentiator, our Dell IP storage portfolio is there to unlock value. In this age of AI, an organization's greatest competitive advantage is how it utilizes its data. How an organization manages, secures and scales its data will increasingly separate the winners from the laggards. To be successful, organizations must be able to streamline disparate data silos, provide seamless data access -- streamlined access and provide premium high-grade data to support AI workloads. The Dell IP storage portfolio is there to help customers realize the value of their data as a competitive advantage. As we talked about Dell Technologies is, by a wide measure, the leading provider of data storage infrastructure and software with very deep enterprise relationships. This scale gives us unique insights into how customers are navigating a world where traditional and modern workloads must coexist, positioning us to guide the future data architectures that will redefine how customers think about their data. To start, the simplification of IT is an absolute must. Businesses around the world are moving to a multi-hypervisor environment, supporting virtual machines, containers and bare metal. This requires flexibility to avoid lock-in and a disaggregated infrastructure of compute and storage, each independent -- each scaling independently in order to provide a 22% reduction in cost versus a more inflexible and more costly HCI solution. This is where our traditional portfolio of PowerStore, PowerFlex, PowerProtect Data Domain All-Flash, augmented by the Dell Automation Platform come into play to help customers modernize core business applications with flexibility, efficiency and with resilience. Let's start with PowerStore, the world's leading mid-range storage array, with a modern, container-based operating system and the industry's only 5:1 data reduction guarantee, PowerStore is the gold standard for enterprise storage. Named #1 in innovation and ease of use, PowerStore is now trusted by over 17,000 customers globally and has grown double digits in revenue in each of the last 5 quarters. PowerStore also comes with access to the Dell Automation Platform, which greatly simplifies infrastructure automation -- infrastructure simplicity, allowing customers to deploy their cloud operating system of choice across a wide variety of PowerEdge servers and PowerStore, ensuring agility, control and tremendous scale by being able to expand compute and storage independently. Next, PowerFlex Ultra, our latest software-defined storage release, greatly improves storage efficiency and reliability delivering 10x9s of data availability and up to 80% storage efficiency, PowerFlex Ultra helps customers to reduce cost by maximizing resource utilization while providing robust redundancy without unnecessary replication. Next, PowerProtect Data Domain All-Flash delivers world-class cyber resilience with speed and efficiency. With up to 544 terabytes of usable storage per node, it delivers 4x faster restore times, 2x faster replication, 80% less power consumption and 40% less floor space. And for artificial intelligence, as Jeff said, we've introduced the AI Data Platform, which is powered by PowerScale and ObjectScale, our data engines, our engines for unstructured data. This platform is designed to deliver the performance, scalability and security to support AI workloads and enterprise-wide deployment of agents. Our Dell IP storage portfolio is not only built to win in traditional workloads such as private clouds, emerging workloads related to artificial intelligence and to support all mission-critical workloads across the world with our cyber solutions. This portfolio also geared to expand margins, not just by the selling of more of the Dell IP storage, but being able to extract more value for the solutions themselves. Our storage innovation engine is firing on all cylinders, and it positions us well for future growth, margin expansion and to strengthen our leadership in the data era. And this foundation is critically important as we look to what comes next. AI workloads are scaling from cloud-native companies into enterprise IT, in industries -- data-intense industries such as health care, finance, manufacturing. These deployments will remain hybrid requiring performance, secure, cost-effective solutions both on-prem and in cloud-connected environments. We are in the very early innings of enterprise adoption. And we are making very good progress quarter-over-quarter. We sell to more and more customers on a sequential basis with a heavy focus on compute. But as customers move into production, they will require a full stack solution. And our value proposition here with the Dell AI Factory is very strong. We are one of the few companies in the world that can design, engineer, manufacture, deliver, integrate, service and support fully integrated solutions that include the compute, the network, the storage that is optimized for AI outcomes and to accelerate time to value. Our solutions build on our unique partnerships and the world's broadest ecosystem of AI partners including OpenAI, xAI, Google Gemini, Meta Llama, Hugging Face, Cohere, Red Hat, Glean and so many others. We are the leading partner with NVIDIA and AMD, building token generation engines of all sizes to meet very specific customer needs. We are that one-stop shop partner guiding customers from model selection to infrastructure while providing for data ingest, fine-tuning and agentic workflows that maximize value. Our AI factory is already powering leading industries around the world. A couple of examples. CSX is a leading transportation company. CSX has partnered with Dell to deploy an AI factory that includes XE servers and the NativeEdge operating platform in order to improve operational efficiency and to deploy real-time analytics at the edge to reduce risk at railroad crossings. This deployment underscores CSX's commitment to use AI to do both improve operational efficiency and reduce risk while underscoring Dell's unique ability to meet a very specific use case. Another example, Hudson River Trading is a global leading quantitative trading firm that is powered by AI research and technology. We have partnered with HRT to deploy AI factory that includes liquid cooled XE servers and the M7725. These high-performance systems are built to support HRT's demanding AI and quantitative trading workloads with high performance, scale and incredible compute density. Again, this deployment underscores HRT's commitment to using AI to drive innovation in quant trading and machine learning while underscoring Dell's unique capability to support even the upper echelon of the financial services industry. And we are a trusted partner in regulated industries, addressing sovereign AI needs of enterprises and governments who demand control over their AI model and their data. Our infrastructure allows for data residency, security and compliance with AI mandates without sacrificing compliance. Again, we're in the very early innings of this. We service over 3,000 customers, and we have 6,700 customers in our opportunity pipeline, and we are extremely excited about the expanding opportunity for growth in this area. In closing, our story here is pretty simple. Dell is not simply participating in the era of AI. We are engineering it. We are enabling it. We are leading it. The world's data will double over the course of the next 2 years. And AI is transforming how that data will be used. Dell is the engine behind that transformation. Modernizing data centers, powering AI and securing the world's mission-critical workloads. This positions us well, not just for the next technology cycle, but for durable revenue growth and margin expansion for many years to come. Thank you for the time this morning, and welcome Jeff back.

I can see a lots of you are excited about an ISG. I think the same is true of our client business. I thought maybe we'd spend a couple of minutes level setting about our client business, and then I'll get in to tell you what we're going to do about it. First, if you look at the chart, we've been able to build a business that scaled, adapts to whatever changes the market has gone through. It's quite a resilient business. We've made it through the ups and downs. And one thing I think is consistent is our execution and the discipline of that execution, delivering a steady oping throughout that period of time. And our commitment to value creation and commitment to this business overall. We're proud of the results you see here. We're #1 in very important categories like commercial PCs, workstations and displays. And over the balance of the decade, we have gained share and grown the business. I'd tell you that doesn't happen by chance. We've been at this for 4 decades. We built long relationships or many relationships that we've had for a long time with our customers. We've driven customer-inspired innovation across the entire portfolio for many years. And I've been at this a long time here. Most of our 41 years in this business, I've been part of this. And I'd tell you, while the PC may feel like a device that's been around for a while, we've not seen anything yet. It's role in productivity, it's role in AI at the edge, story has not been written yet and we're very excited about that opportunity. I think about this refresh cycle that we're in. And it's been a little slower than we expected, but it's been steady. It continues. This refresh cycle is a large one. The installed base is 1.5 billion units. Many of the enterprise fleets that are out there deployed today are 3 to 5 years old. And we are just days away from Windows 10 end of life. And there are still over 500 million PCs that need updated hardware to make that transition. So it continues to be a massive opportunity for us. And then the opportunity around the PC, what I call the PC estate, the peripherals, docks, displays, keyboard, mice, cameras, microphones, is absolutely larger than the PC market. So the two of them together present a large opportunity for our business. In fact, the peripheral market is actually expected to grow slightly faster than the PC market itself, which provides tremendous opportunity. And you take that foundation and you tie it to what I think is encouraging in what I kind of led with the AI ISV ecosystem is ramping up. We're beginning to see PCs with NPUs. We now have an ISV community that's taking those NPUs and taking advantage of those capabilities. We're tracking well over 100 of them that are building applications and new usage patterns for those NPUs that will roll out and increase the utility and extend the capability of the PC. We talked about the small language models earlier this morning. They're going to become even better. So again, I think Michael said it or Arthur said it or some combination of us have said it. The models are no worse than they are today. They get better tomorrow, the next day, the next day, and the same is true of the small language models. And customers are actually using them today. They're using them to drive on-device productivity for things like content creation, translation and research, without a cloud dependency. We see retailers using them in stores to do customer assistance like return handling. We see health care providers use them to automate appointments, summarize appointments, do diagnostics, all done on the PC, all keeping that sensitive customer information on-prem. And those use cases are just the start. ISVs are going to deliver more capability, more applications that will embed the need for an NPU. And then we can talk about what comes next, which is agents. So building the base capability of AI in the PC, then the promise of agentic coming and coming quickly only does the following. It extends and expands the utility of the PC, making it essential as we go forward. You put all of that together, we see lots of opportunity. One of the things I've had to answer over our earnings calls is, are we adjusting our strategy, what about our strategy? What about share, growth in the business? And I thought I'd spend a few minutes talking about strategy then ultimately leading to what we're going to do in 3 steps to improve the growth prospects of our PC business. The first is this one, the premium space. We talk about it all the time on our calls. Most important real estate in PCs, the premium PC, continues to be our primary focus. It's where we've invested. We've seen strong results. It's roughly 25% of the market, we have taken share there. This area has grown 6% since 2019. Our share performance is up 3 points. It's where we needed to be. It's what we did, and we took share. However, in the PC business, it is a business of scale and 75% of the units aren't in the 25, and it's the area where we have lost share. And they are very important categories in this area that we should be in, that we have not been in aggressive enough. And my message to you today is you should expect us to be in the categories that matter in the 75% that we have lost share in. You might ask what are some of those? Clearly, there's the premium consumer business. But more importantly, there's the education market. There's the lower price band or emerging commercial PCs across our industry. And in the PC industry, again, this is a business of scale and scale matters, we have to participate. It's not simple. There are areas of the market that we have not been as strong in, and you should expect, we're going to be strong in them again. I wish I could tell you there was more to the strategy. That's it. We're going to be more aggressive and play in the other 75%. We have one in the top 25. That's very important to us. The other 75%, whether that's premium consumer, whether that's the education market, the emerging commercial market that I just described, we are going to participate aggressively. And we can do this and still maintain the operating margin commitment we've made to you 5% to 7%. I know we can. We've done it before, we'll do it again. We're going to lean into these opportunities. We have a great foundation, 4 decades in the making. We're making the adjustments as we speak. I'll talk about a specific example in a moment, but that's what we're going to do. You ask, what are we working on? Well, how are you going to do that? There's 3 parts of our strategy here. Win in commercial, fixed consumer, peripherals. I'll go into a little more detail. But all I want you to remember about the PC business today. Take share in commercial, fixed consumer, sell more peripherals. That's it. We do that, all the numbers on the charts that Dave is going to show in a minute, we're going to get there. How are we going to do that? We're going to regain commercial momentum. We're going to drive for share. Balancing profitability, our commitment hasn't changed. We're going to continue to optimize the portfolio. We're going to make it easier to find stuff on dell.com. We're going to make it easier for our sellers to sell, Dell's sales chat. We're going to make it easier for the channel to sell our products. You might recall, I called it out on our earnings call 40 days ago, we launched the Dell Pro Essential, which was targeted for that emerging commercial market space. And those price bands in developing countries where we see a big opportunity. We're going to continue to focus on small and medium business where we believe we have an advantaged go-to-market model. You'll see us continue to focus there. On the fix and consumer, it's pretty darn simple. We need to improve profitability. We need to have more products to cover the marketplace. So more products, the right product at the right price, in the right go-to-market, whether that's dell.com, whether it's in a retail store or whether that's through the channel, right product, right time, readily available, some marketing behind it to tell people we're in it to win it. The consumer business is important to us. You can't be in the PC business and ignore 45% of units. Message here is we're improving profitability. We're going to play in all price bands in consumer. And the third part of the strategy, I just talked about, we're going to expand margins by selling more stuff around the PC. So the estate around every desktop, a dock, a monitor, a mouse, a keyboard, a speaker, a camera, anything else you want, dell.com branded. We started that work 2 years ago. That work continues. We think it's a big opportunity in this area that we've targeted. It's a $50 billion TAM with accretive margin rates. So again, 3 things I want you to remember, walk away about our PC business, going to take share in commercial, improving consumer and going to participate fully in all price bands, and we're going to take advantage of the PC estate in our footprint in selling and go-to-market capabilities to sell more peripherals. That's it. That's the strategy. That's the story. That's what we're going to go do. We have a strong foundation. Hopefully, you see a simple, elegant but an aggressive strategy. Not happy with our performance, it's going to change. There's lots of opportunity around this marketplace. Our team is excited about it. This is an incredibly important business to us. So the reason I say we're going to change it, David will build upon this. This is our most capital-efficient business in the company. When it grows, it generates significant cash and that cash creates long-term value for our stakeholders. So on that note, I'll turn it over to David.

Good morning. Great to see everybody. So you've heard from Michael, from Jeff and from Arthur, and I'm pretty excited just to tie all that together now and talk about our financial priorities. We've increased our long-term value creation framework and remain committed to our capital allocation plan. So as Michael touched on earlier, our primary focus is threefold: First, drive revenue growth; second, EPS growth faster than revenue; and third, generating strong cash flows, which are largely returned to our shareholders. And when you look back at our track record over the past 4 decades, that's exactly what we've done. Over the past 4 years, we've steadily increased value for our shareholders. In fact, since 2021, we've more than doubled our revenue and EPS framework, all while committed to more capital to our shareholders. So this morning, our new long-term framework targets revenue growth, 7% to 9%, driven by continued strength in AI. EPS growth of 15% or better. Net income to adjusted free cash flow of 100% or better, targeting a return of 80% plus of adjusted free cash flows to our shareholders and an extension of our dividend growth commitment through FY '30. So now let's spend a bit of time talking about how we're going to deliver this framework. So let's start with revenue. We expect revenue growth of 7% to 9%, up from 3% to 4%. This is underpinned by 2% to 3% growth in CSG and 11% to 14% growth in ISG, up from 6% to 8%. Jeff and Arthur have touched on the strategy and a lot of the tailwinds both in ISG and CSG, but maybe a few points just to reemphasize. In ISG, we will continue to drive innovation and growth across our AI offerings. We'll increase our Dell IP mix in storage, and we'll capitalize on the modernization of the traditional data center. In CSG, as Jeff just mentioned, we'll get back to driving scale and being structural share gainers across the company while accelerating both in attach and in profitability. So let's move to EPS. We expect non-GAAP diluted EPS growth of 15% plus, up from 8% and almost double that of revenue. And we have 4 key operational levers to generate the EPS growth. First, we're going to continue to drive durable revenue growth as we grow and take share in AI and across our core portfolio. Second, we'll increase gross profit. And we have clear opportunities to expand rate in storage as we increase Dell IP mix and in AI as enterprises drive more meaningful adoption. Third, we're investing in the company while being focused on simplifying, standardizing and automating and where possible, applying a little bit more intelligence to unlock more capacity, more productivity and more efficiency and that's across the company, whether it's engineering, to sales, to operations, to services and more. And lastly, our share repurchase program, which is both programmatic and opportunistic. Over the past 5 years, we've roughly doubled EPS. And with this EPS target, we expect to double EPS again. We have an operating model that generates strong cash flow. In fact, since 2021, we've averaged roughly $4.9 billion of annual adjusted free cash flow. This starts with revenue, leveraging our go-to-market engine to grow and to take share. Our focus on financial discipline is a true competitive advantage for us, whether that's pricing, leveraging our supply chain, realizing more cost efficiencies. We were [ relently ] focused on working capital and our truly differentiated negative cash conversion cycle. So all in, we expect net income to adjust our free cash flow conversion to be 100% or better, and that sets us up nicely to talk about our capital allocation framework. We continue to target over 80% plus of adjusted free cash flow to shareholders. If you look back at our track record since the inception of our dividend, we've outperformed that target averaging roughly 100% return, which equates to more than $14.5 billion of adjusted free cash flow. We've achieved this with programmatic share repurchases but also times of opportunism, where we've seen these periods of price dislocation. In fact, if you remember back in Q1, we repurchased almost as many shares as we did in the entirety of FY '25. Since the start of that dividend, we've roughly repurchased $10.5 billion of shares, reducing the shares outstanding by over 80 million shares net of issuances. We've also executed on our dividend, returning 12% in FY '24, 20% in FY '25 and 18% this year. And we remain committed to grow that dividend 10% plus or more through FY '30. This is an extension of our previous target, which ran through FY '28. We remain committed to our investment-grade rating and our 1.5x core ratio target. And there's no change to our approach in M&A. We're going to remain focused on tuck-in IP accretive opportunities that can accelerate our strategy. So to recap, revenue growth, 7% to 9%, driven by strong AI growth. EPS target of 15% plus, almost double that of revenue. We expect net income to adjust the free cash flow conversion to be 100% or better. And we'll take this strong cash flow and target returning over 80% of it to our shareholders. We'll achieve this through programmatic and opportunistic share repurchases, along with our dividend, and we've extended this commitment all the way through FY '30. So there you have it, as we wrap this morning. All of the work we've done over the past 4 decades has merely been the foundation of what is now the AI era. This is driven by Michael's vision and our operating model. We have clear leading opportunities across our portfolio from client, traditional infrastructure and AI. We have a go-to-market engine that provides us with unparalleled insights and relationships. We have a supply chain that is world class. We have a services organization that can touch all corners of the world. We're going to take this model, augment it with AI and make it even stronger and more differentiated. All of this culminates in a business and a team that is focused again, a reminder, drive revenue growth, EPS grew faster than revenue and generate strong cash flows. And our target is to return over 80% of that to our shareholders. So thank you. That completes our morning session. What we'll do now is we'll take roughly a 10-minute break. Once we come back, I'll be delighted to welcome Michael, Jeff and Arthur back on stage, and we'll conduct our Q&A session. So thank you again, and we'll see you again shortly. Thank you. [Break]

All right. Welcome back. I hope everyone enjoyed the break. Just a bit of logistics here. We have 2 mic runners for Q&A. Please raise your hand. We'll get a mic to you. Yes, they're ready. We haven't quite started yet. All right. Please ask one concise question, so we can get to as many of you as possible. And please state -- for the audience, please state your name and your firm. With that, let's bring up Michael, Jeff, Arthur and David.

Okay. First question. Let's go with Simon.

Simon Leopold with Raymond James. So I'm happy with the EPS growth, 15% plus is a good number. But I want to get a better understanding of how we get there because we've been looking at the last couple of years, you've been getting good EPS growth, but it's by reducing your operating expenses. That doesn't seem like the most sustainable strategy going forward. So how are we thinking about within that EPS outlook? How are we getting there in terms of margins? What are you assuming on buybacks? What are sort of the key inputs relative to what you've done over the past couple of years?

Yes, I can start. So I guess, first off, the anchor tenant of the EPS is obviously the revenue growth framework. So the commitment in that 7% to 9% range to drive durable revenue growth. So you start there. You build on that with your consistency of our cash and our capital allocation framework. So I mentioned earlier the 100% execution of that over the last number of years. We're committing as part of that to continue to drive 80% plus of adjusted free cash flow back to our shareholders that consistency through that model obviously aids the EPS growth tremendously as we do that. And then like I mentioned earlier, 2 other operational levers as we look at gross profit, we'll continue to add gross profit dollars. A couple of areas of great opportunity, Dell IP storage, like we mentioned, and also as we see enterprises kind of drive that adoption. And obviously, we'll continue to focus. Look, OpEx will continue to scale as a percentage of revenue. But I think that's the beauty of the 15% plus, 4 agile elements to it that makes us really feel content in terms of what that looks like.

Samik?

Samik from JPMorgan. Maybe just on AI. You highlighted the differentiation that you have also both in terms of technical expertise as well as getting to the market quickly. How should we think about when you are aligning yourself to the merchant GPU market, NVIDIAs, AMDs or the like, there's a parallel market in terms of doing customer builds for some of the hyperscalers. Given the technical expertise that you have, how do you think about the opportunity on that part of the market? What would it need incrementally from an R&D perspective for you to...

Well, make sure I understand the part with regards to...

Addressing the hyperscalers.

Pardon?

Addressing the hyperscalers.

Addressing the hyperscaler or custom ASIC market relative to the merchant GPU market itself, how do you think about that opportunity? And maybe just to get a bit further along on the AI path, like you've talked about more mid-single date margins on your AI servers. As you get to the end of the long-term framework, how should we think about the margins there? And is it really a function of the mix between enterprises versus Tier 2s that drives that change?

Yes. Multiple parts. Hyperscalers, well, we always answer the phone. And we will engage. But today are primarily when you look at our -- where we play today is that Tier 2 CSP layer, the sovereign layer and enterprises. Now do hyperscalers use some of what I just described in fulfilling their infrastructure needs without question. But directly with the hyperscaler to answer your question directly, that has not been an opportunity for us to date, but the engineering complexity continues to grow. It goes up. In fact it goes up pretty significantly, if you recall the chart that I showed. So I think there will be opportunities in the future. There aren't exactly today. We have done quite well in the Tier 2 CSP sovereigns. And as you mentioned, over 3,000 enterprise customers, 6,700 unique customers in the pipeline, the business grows. AMD, NVIDIA, for that matter, whomever comes up with a part that a customer wants, we have built the custom engineering expertise to deploy that. Big racks, little racks, complex racks, individual nodes that go into existing data centers. I think the engineering capability that we've built across the entire spectrum of this class of deployment, obviously, I think it's unmatched. I think it is differentiated to us in the marketplace, and we'll continue to do. So it's an area back to the OpEx question, we're investing in this. Much of what Arthur described in terms of the cooling is an R&D investment area. These engineering pods, it's been an investment area. We'll continue to invest, and we can operate across that rich mix of customers in the mid-single digits that we've been talking about. We believe that is absolutely where we are. That's where we'll continue to stay. We have opportunities with enterprise. And there's probably not much more to add to that.

[indiscernible]

Say again? Stays in the same ZIP Code that Mr. Kennedy described, which gives us, I think maybe an important part that it continues to allow us to build out a portfolio of broad customers.

Let's go with Wamsi.

Wamsi Mohan, Bank of America. If we look at your long-term framework, it looks like you'd roughly double your ISG revenues looking out 5 years. And in that construct, if you think about the overwhelming majority of that, that's probably going to come from AI servers. Can you help us think through what assumptions are embedded within enterprise in that incremental $50 billion to $60 billion of revenue because your margins, you articulated are mid-single digit for AI servers today, but enterprise should push it higher. So be helpful to get some color around off that $50 billion, $60 billion, how much do you think enterprises could be, especially given you noted some real enterprise AI traction that you're seeing at your customer base?

Multiple phases. Let's see. So first, maybe let's get the elephant out in the room about where we think long-term ISG operating margins are within this framework. You should think of this framework that we're going to operate in 10% to 14%. And that is really a byproduct of the anticipated AI mix that we believe will have in this long-term framework. And we're very comfortable with our ability to hit that. I mean, every quarter will vary. It's a long-term framework. It's an annual framework. So the 1 quarter that it's 9-point something, no alarm flags should go up. It should be -- that's just the balance of what the proportion of the businesses are. But that 10% to 14%, we believe we can operate it, operate within. It is reflective of what we think AI margins will be throughout the period of time. And the opportunity we have with Tier 2, CSPs and more sovereign in time. So that balance of margin while enterprise margins are better. We continue to see opportunity, as I hope we described in the build-out of Tier 2 and the sovereign opportunities. That's probably the best way to answer it. You're right. Much of the growth is on the AI side. We have our core ISG business growing at slightly above the marketplace to take share and the balance of the growth comes from AI.

Let's go over here for a moment, Aaron?

Aaron Rakers at Wells Fargo. I'm going to apologize for this first part. But the 15% CAGR, 7% to 9%, can you just level set us? Is that fiscal '26 to fiscal '30? What's the time frame that's being used here? And then my question is shifting over to storage, the storage market, the storage revenue for you guys have been roughly flat over the past handful of years. I think one of the things that I've heard you guys continually talk about is the Dell IP mix versus maybe the non-Dell IP mix, if you will. How do we think about that? Where are we at today? What are you assuming in the model? When does that maybe start to inflect or we actually see some growth in storage? And where do we stand on Project Lightning? And I'll end there.

So maybe I'll take the first piece of that question. So you should consider FY '26 as the baseline for the model. So the last guidance we gave on August 28, and our framework runs through FY '30. So it's over the next 4-year cycle based off that.

So to the revenue question, as Jeff said, in our long-term growth framework. We're looking at share gain. Your question is, hey, trajectory hasn't been there? What's changing? Over the last couple of years, we have worked really hard to do two very specific things. One, simplify the portfolio on the things that matter most to customers. You'll hear me talk over and over again around enabling private cloud infrastructure, enabling AI, enabling cyber resilience. We have done a much better job of focusing our R&D on those activities that matter to customers. Second is the transformation that we're running within the development community. In my prepared remarks, I talked about the fact that our mix of software developers that are focused on product delivery is up 18%. I talked about the fact that we are now entire software development apparatus is united under one agile model, common CI/CD motion. This allows us to work a lot more effectively together delivering features for customers. And if you take a look at what we're doing on private cloud, PowerStore, when are we going to see growth? We've seen it for the last 6 quarters, the last 5 of which has grown double digits, all-flash, when we're going to see that. We've seen it over the last several quarters. In Q2, we grew 25%, 25.7% to be exact, gained 244 basis points of share. So in the areas that matter, we are starting to see growth. Project Lightning, I said is going to be the -- by the way, now that I'm thinking about it, I did not say this in my prepared remarks, so I'm going to get [indiscernible] for that later. But Project Lightning will be the fastest parallel file system in the market with twice the level of throughput as our nearest competitor with 67% greater access. So when you think about the Tier 0 sort of application that a lot of the Tier 2s are upper echelon of the enterprise are looking for, this is going to be perfect for them. And then you have the Dell Data Lakehouse, which is again, the ultimate ingest engine for pipeline orchestration across all storage protocols with seamless life cycle management. And then we augment all of this stuff with the Dell Automation Platform. So we have really focused and streamlined the storage portfolio on things that matter. We've revamped the development engine to deliver innovation to customers faster. So that's why we have confidence, and we've seen a couple of quarters of good progress.

And Aaron, maybe to add color to that is, we have the VXRail HCI headwind I know that's what you're referring to. We continue to work with our customers. We're providing our Dell Private Cloud off-ramp for that with a disaggregated architecture that Arthur just talked about with our Dell Automation Platform that brings that manageability, simplification, ease of deployment to building private clouds with our traditional storage. We believe that continues to play out through calendar '26 or fiscal '27. And as we get towards the end of fiscal '27, you should begin to see the inversion, which I think is your question, you're getting to that the Dell IP portfolio can shine. That's what we're moving towards. And correct me on Lightning in customers' hands by the end of the year?

Beta in the second half in the customer's hand, GA at the beginning of next year -- yes, fiscal year.

Okay. Erik?

Erik Woodring with Morgan Stanley. I'd love Arthur to dig into the AI infrastructure opportunity outside of AI servers. We've talked about the opportunity for attach a number of times. And I just -- we're 2 years beyond the 2023 Analyst Day. I'd just love your kind of updated view on where there is opportunity for attach in storage and services, where there isn't an opportunity for attach in AI infrastructure with storage and services. And what that all means when we think about the broader opportunity behind what has been clearly been a massive growth driver and will remain a massive growth driver for you guys?

Yes. So good to see you. Thanks for the question, Erik. We've been talking about being in the early innings for a while. And I think Michael said it in his prepared remarks and Jeff hinted at it as well, the opportunity for the enterprise is significant. But what we've learned over the course of the last 2 years is that deploying AI is not just a shift in technology. It's a shift in culture. It's a shift in mindset if you think about what Jeff drove within Dell, it was all around bringing our processes together, standardizing -- streamlining, standardizing, automating those processes to really get the value of the solution because, obviously, data is the fuel that feeds AI. And if your data is siloed, is dark, is not observable you're not going to put into the engine, the fuel that it needs to run. And enterprise customers are running their POCs to really focus on compute, playing out, hey, what does this efficiency gain actually look like? But as they move into production, as I said, there will be an incredible opportunity to attach the network and the storage as a full Dell AI Factory. Again, you think about what does a future data center look like? It is one where the silos are broken. The data is connected. Everything is flowing seamlessly. Today, 50% of a typical enterprise data is dark, okay? That means they don't know what it is, right? There's another 30% that sits in cold storage and archive and backup. You can envision a world where all of that becomes observable, active and sitting in hot and warm tiers constantly in circulation feeding AI engines. So again, I think as more and more customers move out of POC into production, you'll see a greater opportunity for attach. But I want to make clear that an enterprise doesn't wake up and say, I'm going to go buy an AI factory. I'm going to go deploy it. I'm going to see results like this, right? There is work that they have to do inside of their enterprise in order to prepare to make that infrastructure useful. And that effort is a lot more than people thought 2 years ago.

Ben?

Ben Reitzes from Melius Research. I wanted to ask just in general, Michael, now that you reflect and you have the structure of an AI business that's really catapulting the enterprise business and you're in PCs and you've benefited from scale. Now in the AI revolution, can you just talk about the benefits of keeping it all together, having PCs, are you seeing the benefits of scale? Is there anything you think you could do with the portfolio long term as you look at this transition? And does it make sense to be in PC still or any other changes that you might want to predict in the future just as you look at your portfolio in terms of the structure of the company?

Yes. I think when we look at our business over time, it's pretty -- it's clear that over the last decade, we have benefited from having everything all in one place with a large number of enterprise and commercial customers. And I think the strength of our supply chain, the relationships with the component suppliers, all of that has benefited because of the scale. And so we don't see any change to that. And as far as the portfolio goes, as David mentioned, you may see smaller tuck-in kinds of things, but we don't see any massive opportunities outside of that. And it continues to be a benefit to us to be able to provide customers a complete set of solutions across client, server, storage, and increasingly, the networking that goes into data center from top of rack. And of course, the services and financing goes all around that.

Let's go with Amit.

Amit Daryanani, Evercore. I guess Michael, Jeff, you both have talked a fair bit about deploying AI internally. It's been a key lever for you guys to get all this OpEx savings and headcount reduction. Can you talk about how far along are you in that journey? How much more do you have to go in there? And is there an end state that you envision that could happen in? Maybe if I just extend this a little bit further, hopefully, you guys will do better than the 7% to 9% growth you've outlined, at least from the AI data points, so it's going to be better. At the same time, you want to cut a lot of OpEx out of the model, a lot of employees out of the model. How do you ensure the guardrails are in place that things don't fall off the rail and you don't have operational execution issues because it seems like you're growing very fast and you're cutting OpEx at the same time, which is impressive and unique but also scary to an extent?

Maybe I'll start, and then you can build on top of that.

Sure.

First of all, we should start with the premise, not all of our OpEx is created equal. The OpEx that's in the 4 distinct categories that differentiate us, our go-to-market engine, our end-end solutions, our supply chain and services is areas we've invested in. So as we have discussed reducing our expense and you can see it in the numbers, we've actually invested in more coverage. We've invested in engineering. We've invested in more platforming, if you will, to build a substrate across the company. We have put the efficiency challenges on the support functions across the company. And we've been able to achieve that with tremendous productivity and efficiency out of some of the simplify, standardize, automate to start. Once you finish that at intelligence, AI. And maybe to bridge to the other part of your question, we've done that reasonably well. We haven't even touched agentic yet. Now do we have coding agents? Yes, we have some of those real life implementations today. But the broad notion of an autonomous agent working across the Dell substrate to do work that should be done by machines, not people. We are in the very, very early innings of that. And we're not going to be ever done. That's not how we run the place. It's continuous improvement, what's next? Pleased but never satisfied. And we think there's opportunity. But at the same time, you'll see us invest. It's in the framework that David described, operating leverage as OpEx as a percentage will continue to go down. But we will invest where we need in the business as appropriate. I'm actually -- I think I'm not concerned about that. We've been long-term operators for a long time. We know how our model works. We know what it takes to run our at-scale supply chain to be able to service in 170 countries to be able to have a go-to-market engine of tens of thousands of sellers. And what I'm working on is how do I have less support of every seller, so we can actually have everybody in sales be a seller. What we want is everybody in engineering to do engineering, not support work. Those are the opportunities and maybe the nuances that we don't describe enough that ultimately, the number of engineers actually doing development is growing. The people supporting them is shrinking because we can be more efficient. The number of sellers is growing, but the number of people that support selling is becoming more efficient. Does that help? So it's driving that level of rigor and discipline in the system. And for the foreseeable future, I see our ability to ultimately shift the OpEx profile. We will make a pretty dramatic shift over this course from what we used to spend in G&A to what we -- we'll spend it in G&A in the future, and you'll see our R&D expense continue to become more productive.

And look, we have a well-established set of KPIs and metrics that help us understand what kind of resource level we need inside the business. I think the key point is that our people are becoming much more productive and capable and we're able to scale our OpEx. And so as you think about the revenue growth that we're adding, well, if our people have better tools, but we don't necessarily need add people in order to achieve that revenue growth, we can make our people more productive and more efficient.

If an engineer can write 40% more code faster and it's higher quality. That's more output.

And look, I mean, I think there is a part of this where we have completely reimagined a number of these activities inside the business, and that is a more complicated kind of multiyear effort that Jeff has been leading, and we've made a lot of progress there. And that involves getting all the data together, thinking deeply about the processes and not being stuck in sort of, well, we used to do it this way, 5 years ago or 10 years ago, so we're just going to make a better version of that. Now it's like, what can it look like given all these tools.

Let's go with Mark.

Mark Newman from Bernstein. So Michael, just going back to earlier in the presentation today, you talked about the huge opportunity ahead in AI. So I think there's no doubt that the opportunity ahead is huge. I guess just taking a step back a bit. A lot of investors continue to worry about short-term digestion concerns. And I just wondered what the executive team at Dell look at for the signposts that the short term is also still strong. Signpost can give investors more confidence that there is no short-term digestion concerns, at least in the near-term horizon to kind of dispose some of those fears. And related to that, the enterprise -- traditional enterprise market, is, as you said, really the bread and butter of Dell. And it's great to see 3,000 enterprises engaged with Dell for AI servers. Any kind of analysis you've done on what percentage of AI workloads in the future will be on-prem versus in the cloud to give us some kind of understanding of where this market is growing longer term?

Arthur, do you want to address the first part of that?

I will. You think about how much we shipped in Q2, and it really didn't even dent our next 5 quarter pipeline. The level of engagements that we have with customers continues to go up and go up significantly when we take a look at the next 5 quarter pipeline. I spend a fair amount of time with some of the largest customers as well as with some of these enterprise customers. They have very ambitious goals, and they have very, very ambitious time lines. If you look at how fast we are being asked to design, deliver, integrate and bring up massive clusters it's something that if you would have asked me 3 years ago, can you do this? I would have said, not on God's green earth, that's how fast things are moving. So we have a really good pulse. As you can imagine, we're involved in every single large deal that you can think of and ones that you can't think of. So we have a pretty good idea of the sort of the pulse of the demand that we see over the next, say, call it, 12 to 18 months. And there is no slowdown that I can see. So that was the first part of the question.

Yes. I think in general, in the infrastructure space, there have been periods where there's been kind of a digestion cycle. But if we look at what's going on with the incredible growth in tokens, we don't see any signs of that. And as Arthur said, the request and the demand signal that we're seeing across a wide range of customers suggest that there's still a lot more demand than supply and it doesn't seem to be slight. Now will there be digestion periods in the future? I'm sure there will be, right? But we do...

No, that's where it's going to go. I mean you asked for signs. During digestion periods, what happens? Orders are canceled, projects are delayed, spending slows down. None of those 3 exist today in that space. In fact, it's just the opposite. We need more faster. Our anticipated computational needs over the next handfuls of quarter. Arthur and I look at one another and go, "Oh my gosh." We have to get a supply chain ready to meet that. We do not see those signs that are historically been there when you see the economy change, you see PCs, you see SP stop buying, you know the pattern. When you see after a buildup of infrastructure for 3 years, you see a pattern. Those patterns with their signals don't -- are nonexistent today. As Michael said, that doesn't say they don't come, but they don't exist today.

Yes. And I think it's important just to add on to that, there is a significant demand. That demand is not always linear. I mean we've been saying this for like 2.5 years, and I want to emphasize the point that while we work on these deals, design, there's a lot having to do with technology readiness with factory readiness. So it's not like the demand is linear, and you can kind of see growth from quarter over quarter over quarter. But as we look at the next 5 quarter pipeline, as we look at how serious customers are about what their aspirations are, I think there's a lot of opportunity over the next 12 to 18 months.

And the number, say, right? Last year, we sold $11 billion worth of AI servers shipped, roughly $10 billion. First half of this year, I think we've sold $17.7 billion and ship $10 billion in first half.

Then the last part of your question with the enterprise versus cloud mix, I don't think anyone knows the answer to this, but a couple of things we're seeing. First is I think the buyers are more experienced, having gone through the sort of original cloud activity kind of everybody loves the public cloud, right, until they get the bill, right? And they've got the bill, and they've understood that, yes, it kind of works for some workloads, but not all workloads. And the bigger, more sophisticated ones, certainly like the arbitrage of multi-cloud and on-prem and colo. And that's where we're seeing a lot of activity with these 3,000 AI factories. And the forward trajectory there looks quite promising. But I don't think anybody knows sort of how much the whole thing is going to grow. But certainly, we're in a great position to be able to capture a large portion of...

Maybe a couple of reinforcing facts. One is we think it's hybrid. This is no doubt a hybrid world. The data is on-prem. The data is being created at the edge. So we see a continuum. There will be hyperscaler public cloud AI. It will be on-prem in the data center. It will be out at the edge in a factory, and it will be out at the far edge on a PC, which is why back to the question that been asked, you look at the continuum of AI, it's making its way to the PC. So from the large-scale clusters out to the edge, that continuum of AI, we believe, exists and you'll see AI -- or you'll see a hybrid. And quite frankly, we see -- I think AI follows the data, where the data is created is the most efficient place to do the computation.

Yes. Can I add on to that?

Sure.

Another point I would want to make there is we talk a lot about the modernizing of data centers. What I think about a lot is, traditionally, data centers in that defendant any CIO, largely thought of as a cost center, right, paid to keep the lights on. How -- when you think about something as a cost center, what do you think about, you think about how am I going to optimize this cost? When you think about data centers or all infrastructure in the future, it's now a value center because it's housing your most valuable asset, it's housing your data, it's housing your AI. Customers are starting to think a lot differently about what their data centers need to look like, and they're starting to shift from, hey, I used to think about this as a cost center. Now I need to be thinking about this as a value center. When you start thinking about it as a value center, now you're not thinking about how to optimize it, you are thinking about how do I invest in it because this is actually driving real value for the organization.

And maybe to wrap just one more piece of it just from a financial perspective. So from my perspective, you mentioned some of those gating factors I expect this business to be margin dollar accretive, right? So Jeff mentioned earlier, mid-single digits in terms of operating income. That's nonnegotiable. That's our commitment as part of the framework. We'll have control points kind of tied to that. So we're creating value across the enterprise.

Let's go upfront with Asiya.

Asiya from Citigroup. Just David, a question investors often ask about free cash flow. There's this view that the growth of AI is heavy on free cash flow generation just because of net working capital. You clearly talked about committing to your free cash flow conversion today. So just maybe help us understand how you're kind of managing that because the view, again, within investors is that it's AI business growth? Is net working capital intense?

Yes, for sure. So like we said, a truly differentiator for us, has been for the 40 years of the company. Look, as you stare at AI growth, you take it in the piece parts, right? So Jeff mentioned earlier, our CSG business, truly capital efficient as we drive through that. I will probably layer on top of that. Our core server business as part of that as well. Obviously, storage and AI are less capital efficient but are still efficient in total. We also have the opportunity because of our strength of our balance sheet to be able to invest from time to time in the inventory required for AI. But when you look over the lifetime of the framework, that's why we felt comfortable with the net income to adjusted free cash flow of 100% or better. We see it continuing all the way through the life of the framework to be that differentiator for us.

Okay. Let's go in the middle here in the back, Mike?

Mike Ng from Goldman Sachs. I wanted to ask about Dell's competitive advantage in AI, which I know you commented on earlier. Relative to other OEMs, are there one or two things that consistently stand out in the pursuit of AI cloud deals? And then given the potential for kind of U.S. public sector workloads in AI, does being a U.S. company help in some sort of way as well?

I'll take the first part of that. I hope I communicated, I'll give it a go again. What stands out is our engineering expertise, the time to design, the rapid scale deployment to get that gear once the design is done on the dock at the customer. When it shows that it works, it's in the data center. It works 99-plus percent of the time, and then we cover that with installation and support services, one to deploy it, to install it and to keep it up and running, that consistently has differentiated us in the marketplace against all OEMs and ODMs for that matter in the marketplace. And that's what we'll continue to invest in. That continues to be something our customers come time and time back to us. And mind you, we're not -- we're never the lowest price guy. We're driving differentiation with those items that I described and you add financing for those customers that need the financing help or support. And that comprehensive portfolio or package of capabilities is what has consistently differentiated us from the very first day. And Arthur and the team have done nothing except double down and put more capability in place.

Yes. And that's consistent with what we heard from the customers. On the U.S. government question, we don't expect any orders today because the government shut down, I think at least it was when we started the meeting. But the gap between the private sector AI capabilities and what you find in the National Labs and Department of Defense and other intelligence services has kind of never been greater. And this has been realized and there are a number of efforts underway to close that gap. And certainly, we have had a great relationship with the government as a customer over many, many decades, and we're in great position based on the opportunities that we're engaged in and hearing about.

Let's go upfront again with Tim.

Tim Long at Barclays. Just wanted to touch on AI servers, maybe 1A and 1B. Really interesting data on the efficiency of the new 16G, 17G servers and how old the installed base is. I feel like, historically, this vertical has seen a few really good years, a few really bad years, and we're kind of flattening out. So do you think there's anything about the refresh opportunity ahead with this dynamic? And maybe AI and the need to maybe to upgrade more than in prior cycles? So maybe what's the tail of that dynamic? And then just really quickly, you mentioned, I thought it was very interesting on the AI enterprise servers that it would be new budget from enterprises. Just curious if there's any proof points for that because I think, generally, enterprises spend x on servers, they're going to spend x on servers and change the mix. So any color on how do you think AI would be incremental to that bucket?

Sure. If you start with and Arthur hit this in his presentation, I know he'll add to this. There's a consolidation opportunity. There is a large number of old servers deployed. Customers are looking for space and power for AI and you have an opportunity to create that floor space, power and cooling capacity by taking an old server at 16G 3:5:1 on 7G 5:7:1 conversion rates of taking advantage of more cores, more power-efficient cores, bigger memory arrays to consolidate and free up space, power and cooling for other stuff. We've been seeing that, that continues. We believe that's the opportunity, and we're seeing AI -- we're seeing IT budgets shift to AI. I would think, and Arthur will add to this, but customers don't -- I thought Arthur hit it correctly is it's not an IT project. When -- here's how AI is getting deployed at scale by successful companies. It's not an IT project. It's a business imperative. When it's an IT project, it struggles. If it's a business imperative, if I could give you 40% productivity, would didn't you give me a couple of dollars to go get it. That's how CEOs, how boardrooms are talking about AI as being disruptive in a game changer and why you see that as if you can give proof points and there's a return on investment, you can get incremental dollars to provide that.

You answered it, Jeff.

Yes. On the [ PowerPoint ], I would say if you think about certain markets like in Europe, where the cost of power has gone up dramatically, the savings is tremendous. And that has fueled a faster than normal refresh because -- and I would say, faster than normal growth rates in Europe because the ROI in moving to 17G with the lower power consumption is dramatic. So think about countries where the cost of power has gone up significantly.

Let's go over here again, David.

David Vogt at UBS. I just wanted to go back to the sovereign and enterprise opportunity. So when you talk to the 3,000 customers that have deployed AI factory and potential new customers, what are the governors or what are the gating factors that are holding them back? Meaning, why can't they move faster? Like what is the issues at data storage? Is it complexity, compliance? And then I'll give the second question at the same time. When you think about -- in the past, there's been opportunity to be competitive on big deal, signature deals. I think Arthur mentioned that you're going to be involved in pretty much any deal that comes to market. Is there anything in sort of that enterprise sort of or sovereign vertical that kind of jumps out at you is like these are landmark transactions, landmark deals that really maybe change the competitive dynamic or competitive intensity of those types of potential deals that you may target going forward?

Maybe to answer your question, I'm not worried about the 3,000 customers. They're already started. I worry about the tens of thousands of other customers that haven't started. And those 3,000 are doing their pilots, they're moving into production. They are starting with a use case, and they add another use case. They see return. That momentum is going. We're seeing repeat buyers we're seeing the growth of our enterprise portion of AI grow. We've talked about that. It's how do we get the others moving. And it really is this notion about where do I start? Where is my darn data? Can I do anything with my data? How do I pick a use case? And ultimately, can I get a return on the use case that I pick? And our professional services, along with our partners' professional services are helping customers work their way through that. That's the biggest inertia to plow through because once you have found a use case and you get whatever the productivity you chart it out to do, it's -- you're a believer quickly. This notion that I described earlier, where AI is being driven by the business works at a much greater rate than of its being an IT project. "Hey, we had AI, it's out there, help yourself pick a model." Or it's being driven by the R&D leader, I met with a large manufacturing company 2 weeks ago, their entire engineering leadership and we talked about coding assistance and how they could use coding assistance and the results that we've seen inside our company, it's holy smokes. I got to get me some of that. How did you do it? Well, we started with this developer pool, we got that developer pool, we built momentum and da, da, da. So it's teaching and it's getting that practice out that we find as the biggest opportunity. It's why our services and our partner services organizations are helping customers navigate that. That's, in my mind, for enterprise. And then we talked about it, once you now deploy a node with a GPU or 4 GPUs or 8 GPUs and you begin to see, you can start talking about networking. You can talk about storage. So for example, our next best action started with storage all over the place. It now works on our power scale and object scale unstructured assets. We're getting incredible performance. It's driving productivity for our field service organization. Real life example, we are teaching customers about that. That's where the opportunity back to Erik's question about dragging around enterprise. Once you get a proof point, you begin to hook up data and networking and services is where you get the more revenue around each and every AI server. Does that help?

[indiscernible]

Competitive intensity?

[indiscernible]

Well, you know this is kind of a big category. Everybody shows up. Particularly in the big deals, everybody shows up, OEMs and ODMs. In enterprise, specifically the relationships that we have with our business over many, many years, serves us well. We serve small business, medium business, public institutions, large corporations and multinationals with a very large sales force and partner network. I think that reach is absolutely advantage, the service capability that goes along with it and a reputation of putting high-quality gear into their infrastructure.

Yes. We're -- as you heard about our market-leading positions, we're generally the incumbent in enterprise and commercial. I think the other point is that there is a varying rate of sense of urgency across companies. And we've approached this with a high sense of urgency. And I think it's not there across all industries. But I think as the use cases become more apparent and as it becomes clear, what can be done in R&D and sales, in customer service and key functions, then it becomes -- it's driven from the business line executives, from the CEO, from the Board what are you doing to take advantage of this great capability.

Okay. Let's go up front here. I'm sorry, I can't see with the light right next to Simon.

It's Mehdi Hosseini, Susquehanna International. You talked about ISG operating margin target of 10% to 14%. What's the underlying assumption for the storage mix? And as a follow-up to it, this event is mostly focused on AI compute accelerator. Jeff, you talked about architectural changes that need to happen to storage. I haven't really heard much details about your storage. You briefly talked about portfolio. And I'm asking this because if storage is going to be a key mix behind ISG target -- operating margin target of 10% to 14%. And if data is going to be the key what's going to enable inferencing? I don't hear of the strategy, how are you going to go and procure the key components. Everybody is focused on component compute accelerator, hard disk drive vendors are talking about 2 years of sold out, 2 years of backlog, I don't hear you talking about securing components. And as you know, these are semiconductor manufacturing. Long, long lead times, and they're coming out of a 2, 3 years of a deep recession. I don't hear them rushing to add clean room capacity. What are you going to do about it? If a storage is going to be a key component part of our ISG?

I think that's 3 or 4 questions. Let me work my way through.

They all related.

We occasionally buy some ingredients for storage. I'm pretty sure.

Yes. So first, in the guidance of 10% to 14% operating margins the implied storage growth is slightly ahead of the storage market, taking share. So revenue growth ahead of the market, which is mid-single digits. You should expect our -- it's actually low to mid-single digits depending on which storage category, you should expect us to outperform that and revenue growth. What we've communicated consistently is, as our portfolio shifts to more Dell IP, the margin profile of the storage business improves. We expect that and modeled that over this time frame. Correct me if I go astray. I think that's the first part of your question. The second part of your question around storage, perhaps we didn't -- weren't clear enough. We are the storage leader in the market, period. Larger than number?

Two and three combined.

Two and three combined. So we have a large footprint from block assets, file assets and structured assets, data protection assets. If you step back, the storage strategy is really around 3 pillars. The first pillar is helping customers build private clouds. Think of that as traditional storage, think of that as taking our block assets helping customers where we have a leadership position and helping customers easily implement that. Arthur and I both made references to the Dell Automation Platform that's tied to the Dell Private Cloud that allows us to take the ease of use of what we created in HCI and bring live to customers deploying large-scale traditional storage. The second component of our storage strategy is the Dell AI storage. And if you think about that, we tried to describe that as our unstructured assets because a lot of this data is unstructured, 80%, growing 55% annually. All of this data is coming at us. So we take our at-scale PowerScale and ObjectScale assets that are known for their performance, flexibility, manageability in the marketplace. That's the foundation. Then we add Project Lightning, which is our parallel file system. We had Project Dynamo, which is the KV cache to drive inferencing to improve inferencing performance. We put the Dell Data Lakehouse around that, which helps ingest data into AI systems. We package that up as the AI answer for storage. And then the third component of our storage is around data protection and cyber resiliency. So 3 pillars of our storage strategy more of a traditional approach, but making it much easier in helping customers build private cloud on-prem. An AI component, which will have the assets that I just described. And then third, around cyber resilience and data protection. We are a leader across the board there. And we're investing in the R&D, many of the coding assistance and knowledge assistance that I talked about continue to help accelerate the delivery of features and capability. And then the last part of your question, I think our supply chain does okay. I think we know how to operate in when there's lots of supply when there's no supply and everything in between, we have long, long, long relationships with the disk drive manufacturers. I met one of the CEO of one of them just last week talking about what's happening. I think we're in pretty good shape when it comes to DRAM, NAND and spindles.

And to that question, it sort of relates to one of the earlier questions that was asked about the portfolio. Having a #1 PC business in revenue, #1 in servers and #1 in storage. Obviously, our consumption of NAND and DRAM and disk drive specifically is among the largest in the world, right? And so those -- that scale plus the long-term relationships, we feel very comfortable in our ability to secure the supply we need.

Okay. Let's do one more, and then we'll ask Michael to close with Vijay.

Vijay at Mizuho. Just a quick question on the -- back to the ISG side. I think this fiscal '25, '26, you guys grew ISG like 25%, 30% year-on-year. I know you're guiding to 11% to 15% going forward. Are you being conservative there? And then as you look out to fiscal '30, I wonder what the mix of AI servers to storage and networking is. What are you modeling internally, I guess?

I mean I'll start maybe just to reiterate the framework, again, just I think Jeff touched on it briefly. So within ISG, we expect our core businesses, core server and storage in that mid-single-digit growth, which would be at or slightly ahead of the market. Do the math within that, you get to an AI number that's between 20%, 25%. Lots of different opinions of how big the AI TAM is, right, as you kind of go through that. Arthur mentioned it earlier, we are in all the conversations when these deals pop up across CSPs, sovereign and the enterprise. So if in your scenario analysis, it's a bigger market, we're there to play. We're there to add accretive margin dollars, which is accretive to our OpInc, which contributes to EPS. So we'll be there if it's bigger, but we think we have a solid framework up through FY '30 right now.

And the second part of your question, I don't remember.

[indiscernible]

You just said it.

Yes. I just...

Okay. Sorry, sorry.

Okay. Let's close out. Thanks for everyone's questions. Michael over to you.

All right. Thank you all very much for being with us today. If we reflect on the past 2 years, it's clear the pace of change and innovation has been unprecedented. And there's a pretty good chance we could be saying the same thing 2 years from now as the cycle continues to accelerate. And at the core of that, of course, is AI and data and infrastructure are advancing at an exponential rate. And Dell finds itself at the center of that transformation. We have raised our targets across the board, now targeting 15% plus EPS growth, and we're committed to generating substantial free cash flow, as we discussed, with the majority of that will be returned to our shareholders. Our strategy remains the same, and our track record of value creation over the past 4 decades speaks for itself. Looking ahead, we're even more excited about the opportunities that we see. Thank you again for joining us today.