Hewlett Packard Enterprise Company (HPE) Earnings Call Transcript & Summary

Great. Well, thank you, everybody. Welcome to the Hewlett Packard Enterprise keynote fireside chat at the Goldman Sachs Communacopia and Technology Conference. I have the privilege of introducing Antonio Neri, who is the Chief Executive Officer at Hewlett Packard Enterprise. Prior to becoming CEO in 2018, Antonio spent 23 years in various leadership roles in the combined Hewlett Packard Company, including as President, Executive Vice President and General Manager of HP's Enterprise Group. Before we start, I want to read a disclaimer on behalf of HPE. Antonio's remarks may contain forward-looking statements, so please refer to the company's SEC filings, including its most recent Form 10-Q for a discussion of risk factors that relate to its business. My name is Mike Ng, and I cover hardware and communications technology here at Goldman. We have about 35 minutes for today's presentation. So first, thank you so much for being here, Antonio.

Thank you.

It's really a privilege to have you on stage.

So you've had a front seat to some of the biggest transformations, both at the company but also the technology industry more broadly. I want to ask about the technology shift that we're undergoing today, which is really about accelerated compute. First, how would you characterize this technological shift relative to prior ones, such as the initial build-out of public cloud? What can we learn from the past? And in what ways is AI different?

Well, thank you, Michael, and good morning, everyone. Thanks for having me today here. My generation is my -- where I come from, I think I have lived all the major inflection points that -- in the history of IT and computing itself. Obviously, from the original mainframe to PC client servers to mobility to cloud and today, AI. And I will say that this feels a little bit different. There are little similarities, but they are different. In many ways, in the past was about connecting the world. It was about driving digital transformation of digitized enterprise. This is about transforming the -- really the way we work and, obviously, our own personal lives. I think it's, in my view, is going to be the most disruptive technology, at least in my lifetime, and has the potential to really transform everything, including solving some of the biggest societal challenges we live. And HPE has been at the forefront of many innovations, including supercomputing, which obviously has used some of these unique capabilities for a long period of time. But now with generative AI, it's been democratized and been accessible to all, including consumers. So it's an exciting time, but also we are early, early in that journey. Clearly, today, the companies that are invested in building this large language model -- models are driving a lot of the demand we see for accelerated computing, but we already see a significant uptick in enterprise and sovereign clouds. And I think on the sovereign space, obviously, it's not just a matter of sovereignty. It's about bringing the cultural aspect to this new technology, which needs to be in the context of what those countries represent as well. And so Hewlett Packard Enterprise is well positioned to participate in all of them with our unique innovation and the ongoing investments we continue to make in the business. So again, I led many of those transitions, and this is as exciting or more exciting than the previous one.

That's great. Let's dive a little bit deeper into this AI opportunity. Last quarter, HP Enterprise demonstrated a lot of momentum in AI systems with orders of $1.6 billion, AI systems revenue of $1.3 billion, a backlog of $3.4 billion. How would you articulate HP Enterprise's AI strategy today? And who are the main customers who are consuming HP Enterprise's AI products? You mentioned enterprise and sovereign cloud as an area where you're getting a lot of momentum.

Yes. So clearly, we see a lot of momentum. We are very pleased with our results. And we have done that with a lot of discipline, including the fact that the street is still digesting our results, I will say. The fact that on a year-over-year basis, we actually improved profitability when it comes down to the server business. In fact, we improved our operating margin 70 basis points, notwithstanding that the mix of the server was obviously heavily weighted to the AI servers. But at the same time, we saw great momentum on the traditional servers where also we saw double-digit growth, both sequentially and on a quarter-over-quarter basis. And it's fair to say when you look at the profitability of our server business, it's the most profitable business of the service segment today in the market. We see tremendous demand. Our pipeline is a multiple of what we already converted, and the backlog is very healthy. But as I think of the AI market, I think about in 3 unique segments, and it's important we differentiate those segments. One is the service provider segment, which obviously include the hyperscalers and what I call the Tier 2, Tier 3 segments. The Tier 2, Tier 3 are more focused on hosting. A lot of them are not in the United States. In fact, many of them are in Europe and in Asia. And they are there to serve a purpose, which is basically giving access to enterprises and other customers, and some of them are small language model builders to that accelerated computing. Obviously, that's where a lot of demand is and also driven by these model builders, which consume a lot of accelerated computing and a lot of power. And so getting access to that is very important on a time basis. The second segment is the sovereign cloud. And by the way, the first segment is, you can argue, if you take them, all the builders aside, it's maybe 10, 20 customers at the most. And they are going to consume hundreds of thousands of GPUs, if not, at some point, exceeding 1 million GPUs. The second segment of the market is the sovereign cloud. And think about countries or geographies where they are going to build AI clouds at scale to give access to enterprises and the local customers to that technology. And those are maybe in the tens of customers, maybe hundred, and they're going to consume each time tens of thousands of GPUs because ultimately, that's the size of the systems they're going to deploy. And we already won several of them, including the University of Bristol, the Japan AIST and the like. And then there is the enterprise, which is still in the early stages, and that represented for us in the mid-teens on the order side and the revenue conversion. And that's still early stages, but we see the maturity in use cases taking place. In fact, we see a tremendous traction in health care, in manufacturing and services, in general. And to me, that's a big opportunity. And that's why we're chatting a little bit earlier that at HPE Discover, we introduced what I call an AI-in-the-box solution because the enterprise, they have no time to bring together infrastructure and all the software to develop these AI applications. And that's why our solution, that was co-engineered with NVIDIA, is basically a fully engineered solution from infrastructure to the application to the workflows that enterprise customers need. And that's early, and that's why it's so exciting because ultimately, a lot of those customer need help upfront and on the back end of this, in addition to the technology and the software.

That's great. And I was really impressed by what I saw at HPE Discover with Private Cloud AI, where you're really bringing together NVIDIA's compute, networking and software with HPE's compute storage and cloud capabilities. Could you just expand a little bit more on what HPE Private Cloud AI is and how that may be differentiated relative to what else might be in the market for enterprises?

Yes. As you saw, it's a solution specifically targeting the enterprise segment of the market and the premise was about simplicity and speed, speed to time to value. And that solution comes in 4 pre-configured solutions for inferencing, RAG, small language model training or fine-tuning, and large language model development, which not a lot of enterprises are going to do themselves. They're going to pick a model and then give context with their data. And we thought about that value proposition from how we can speed up the deployment of this technology and help customers in the enterprise space deliver the results on their return on investment. And therefore, it was all about the experience. They experienced us in our HPE GreenLake cloud. Today, we have 37,000 customers on the cloud platform, managing networking, server, storage and all the SaaS associated services, including private cloud. And basically, from there, you just basically, with 3 clicks in less than 30 seconds, you are actually up and running. And we gave the full stack, including the RAG, the NVIDIA AI Enterprise software, the NIMs, including the Agent Blueprints for specific verticals. In fact, last week, we announced some incremental additions to that. And then obviously, it's day 0, day 1 and day 2 all built in the same experience. And the day 2 is as important as day 1, right, when you deploy this. So we feel this is going to be an incredible flagship product for us, and it's part of the GreenLake experience. We deployed that infrastructure, one of the 4 pre-configure, either in a customer data center or in a colo, and we can offer a customer-managed or a self- or a HPE-managed -- minimal managed services. And so the differentiation is co-engineer, ready in a SKU, they can order one single product number versus a reference architecture of multiple components that customer has to put together.

Great. So HP Enterprise is a leader in exascale supercomputing. And I think the company is responsible for manufacturing and selling the top 3 of the top 5 supercomputers, including: number one, which is Frontier; and number two, which is Aurora. Can you talk a little bit about how HP Enterprise's leadership in supercomputing helps support the broader AI strategy? One thing that certainly comes to mind for me is the success that you've gotten in sovereign AI, right? You mentioned a couple of those projects.

Well, I think in general, sovereign AI will look a little bit more like a supercomputer in many ways. But put aside that, I think HPE has decades of experience delivering AI-scaled capabilities in governments, in academia and very high-end enterprises that may need that supercomputer power. And as you said, our supercomputer business has broke the exascale barrier. That took 14 years from the petaflops to exaflop, and the Frontier system is capable to deliver a quintillion operations per second, which is a billion, billion transactions. Think about how much you can do with that and how many problems you can go solve with that. And to give a sense, Frontier today runs on 40,000 GPUs -- 60,000 GPUs and 40,000 CPUs, all in one cohesive system. And the magic of that is actually our networking fabric. We have a coherent fabric, which we call HPE Slingshot, that allows us to bring all of this. And the most fundamental metric that customers look there is the ability to start and end the model without interrupting anything. And so because of that, we now have to build and run this system of scale. But as you know, as we go forward, the amount of energy we're going to consume is significant. In fact, some of the systems consumes tens of megawatts just for one system. And as we transition to direct liquid cooling, which is foundational to what comes next, you need to have the manufacturing and the services capability to do it. And HPE has 2 amazing factories that build direct liquid cooling now for a number of years, which means the capital to build those factory has already been deployed years before. And therefore, we are ready to adopt these new technologies, whether it's from NVIDIA or any other silicon provider. Obviously, we have chatted about Blackwell now for some time. But the reality is we can build and service any system that's direct liquid cooled. Now in addition to that, we have hundreds of patents in that space that makes us unique and different. But ultimately, you have to service the systems and the maintenance cycle of the systems is significantly higher. And that's why I believe, as we transition to this new set of technologies, not only we can deliver better performance which is obvious. But at the same time, lower the energy consumption, and HPE has a number of capabilities and patents there where we might do the maintenance and service, which is an opportunity for us to drive gross margin expansions as we go forward.

Great. So you've got experience in liquid cooling. You have the networking fabric with Slingshot. You're serving an existing customer in sovereign, and you're in a good position to deliver maintenance and services. Are there any other product capabilities or services that you like to offer that you might have to develop in-house or pursue through M&A to service what's required in terms of AI infrastructure demand going forward?

Yes. I think when you think about the next generation of architectures, I do believe the networking component is going to be a core tenant of that architecture. And both through the organic investments we have made and the pending acquisition of Juniper, we will we have an amazing set of intellectual property to drive the next iteration of the architecture for accelerated computing. And I think this is where HPE think about not just the server or the rack, but we think about the whole data center as a package. And that's an opportunity for us once we close the Juniper transaction. In addition, we also have amazing software because one of the magics, I will call it, is the software to manage contention through the networks. So as we continue to grow and chain more and more GPUs together, and our Slingshot fabric already can support 80,000 GPUs in one cohesive system, we also need to make sure that the AI models run very efficiently because the worst thing you can do, Michael, is start the model and stop. That's a waste of time, energy and dollars. And therefore, we now have to continue to run these models to scale without interruption. And that's the value-added services that customers want and need, especially at that scale. As you go to enterprise, you're going to have hundreds of thousands of customers that will deploy these much more standardized solutions. And our opportunity there is the whole stack, including the services piece. But in the end, I don't think enterprises will deploy more than hundreds of GPUs at the time. And therefore, it's all about the speed of deployment and the ability to deliver that time to value, the use case.

Right. That makes a lot of sense. And if I could just ask about your positioning among hyperscalers for AI, I think you've had some notable wins there. But the natural question would be hyperscalers haven't historically worked with OEMs. So why work with HPE for AI?

It was very simple. It was the ability to provide a solution that was, at data center level, ready to run the models. It was not about selling just servers. We offer them, as a part of GreenLake, a complete data center solution for them to run their models. That's why we got that business from one particular hyperscaler.

Okay. Great. And just kind of tying it back to financials, last quarter, HPE reported relatively stable operating margins within its service segment where the AI systems reside, despite accelerating revenue contributions from these AI systems. How do you characterize the margin profile of the AI systems relative to the broader server segment? And what are the opportunities to improve the margins of AI systems over time?

Yes. So at the operating margins level, because we don't disclose the gross margin, we actually improved operating margins by 70 basis points on a year-over-year basis. But the contribution of AI was significantly higher because last year, at this time, we only converted $300 million. This time, we converted $1.3 billion. So with $1 billion more, let's put it this way, with $1 billion more revenue in the AI space, we actually improved 70 basis points on operating margins. And when you need to look at that, it's not just the product aspect of it, it's the services aspect of it. And that's why when you look at our earnings, we start disclosing, in our composition between product and services, how much contribution comes from each of them. And I do believe as we grow enterprise, we grow sovereign and we shift to direct liquid cooling, there will be more services opportunity as we go along the way. And so -- but we are very committed, Michael, to maintain our profitability within the ranges we guided a while back in our long-term ranges, which is 11% to 13%. And so when you look at the total server and AI segment, operating margin was 10.8%, so we'll argue kind of there. But there is obviously a backlog. You mentioned that $3.4 billion. We have an amazing pipeline, and we believe we are operating with discipline, and that's the key here. And I think you need to find the right balance to drive profitable growth in each of the segments and be able to deliver those results in addition to generate the right cash through the working capital because the amount of inventory you have to manage through these transitions.

Yes. I appreciated those new disclosures breaking down AI system orders and revenue by products and services because I think the observation was there's probably 10% to 15% of orders coming from services, but it hasn't really shown up in revenue.

Well, it's all deferred, right? So that's the reality. Now normally, when you sign a contract like that, you can go for 3 years and all that revenue gets deferred over the 3 years.

Right. One area that you called out and was a very -- was an area of strength in the quarter was in traditional compute. Could you talk a little bit about what you're seeing there? Are we at a point where there should be an inflection in traditional compute demand?

Well, I do believe the traditional server or the traditional compute is in recovery. And I think there is a little bit of pent-up demand in the market to modernize that infrastructure. But at the same time, we saw on our side that our transition to Gen11, which is our latest generation of products, although we are already working on Gen12, is accelerating. In fact, we said that 60% of our server business has already transitioned to Gen11. And when you look at the Gen11 versus the previous generation, obviously, it's more dense, structurally has a different set of cost and AUP, and then obviously, we start seeing already a slight uptick in commodity costs. So from that perspective, we feel good about it. In fact, the traditional server grew double digits, both sequentially and on a year-over-year basis. Now remember, that server also goes into our private cloud. And so you get the benefit of selling the server as a stand-alone or the benefit of selling it as a part of private cloud stack. And so in any case, everything needs a computer at some point in time. And also, I believe, which is very important to understand, is that it makes no economical sense to move a legacy workload to a server that has accelerated computing. It's not necessary. It's a waste of resource and dollars. And that's why we have not seen cannibalization from the AI business to the traditional legacy business. I don't think that will be the case, unless you re-architect the application when you build a lot of AI. But the reality is that the AI application in the end will be more used as an inferencing solution than maybe a traditional workload doing transaction type of work.

Great. That's really helpful. And on the traditional server side, are you seeing any notable areas of demand when you think about individual customer verticals, whether that's government, enterprise, service provider or [indiscernible]?

For sure, government. I mean we're a large provider to the government, and we do many things there. I think health care as well, that's another area. But I will say it's more by workload, I would say. One of the things we saw quite a bit of traction is with large OLTP workloads like SAP and others that when you think about the economics of that and think about how much data you need to move, [ for ultimately ], running on the same infrastructure pretty much. Unless you're running out of space and power, it is actually, depending on the size of your instance, it's cheaper to run it on-prem than off-prem.

Okay. Great. Shifting gears to storage. Could you talk a little bit about the transitioning that's happening in the storage portfolio, migrating more to HPE Alletra, some of the mix shifts that are happening between first-party storage solutions versus third-party solutions? Maybe you can just set the groundwork for...

Yes. Well, it's an intentional strategy. Historically, the company, if you go back 20 years, have had a mix of offerings between owned IP and non-IP. And over the last number of years, we've been very intentional to drive to our own IP products and obviously make the R&D investments to drive that shift. This is what we refer as HPE Alletra and Alletra MP, and MP stands for multi-protocol. But from a market trend perspective, obviously, AI will demand different type of protocols and will demand also more storage capacity. So my view is that as we go forward with Alletra MP, we have an opportunity to expand our footprint beyond the traditional market we have been participating, which is the traditional block market. And so we have multiple, I will say, inflection point. Number one is our own installed base to Alletra MP in the block space. We have a fantastic solution that can compete with anyone on price, performance. In fact, in many ways, deliver better performance with a guaranteed 100% availability and type of as-a-service models that people are looking for. But that same infrastructure was architected in a scale-out software-defined way that allows us to go from block to file to object, without changing the infrastructure. And that's a big issue for customers because you don't want to buy this infrastructure for block and that infrastructure for file and/or object and manage 2 different sets of experiences, 2 different control planes. By the way, all of this, we built inside GreenLake. So when you deploy that Alletra MP hardware, if you will, it's a scale-out so you keep adding capacity. And the only thing it just will change is the operating system that runs on it. And that, I call it [ the bits ], are downloaded from GreenLake on the hardware. So you may have this capacity for block and you need more capacity for the object, it's the same hardware, but it's just the operating system that sits on it is now for file and object. But the control plane and the large cycle management is exactly the same and inside HPE GreenLake. So this is an exciting transition for us. And we actually wrap all of that with secondary storage with our own solution with backup and recovery and ransomware protection services with Zerto. And then we have unique partnerships. There are very few now and very curated, where we believe are complementary to what we do but not to offer everything to everyone. And I also will say, while we do all of that, it's fair to recognize that also we are changing the business model. So the hardware piece of this, again, consistent architecture is a CapEx model. But when you download the software, whether it's for, block, file and object, that's a SaaS component. Therefore, on a -- let's say, on a $1,000 average price, a portion of that $1,000 is deferred over the length of the license, which is a SaaS kind of approach. So that's important. Remember that this is also included in the private cloud for AI. So inside, you have a ProLiant server and you have HPE Alletra MP fully certified with NVIDIA. And in both cases, you may have a switch, which today is HPE Aruba for the Alletra MP offering.

Let's talk about Intelligent Edge, a segment of the business that saw a tremendous amount of demand immediately following the pandemic with a lot of network upgrades, but it also led to a period of inventory digestion by customers. Where are we in that inventory digestion period? I believe we're mostly done with it at this point, but could you just share what you're seeing from a customer demand perspective in wireless LAN and Aruba?

Well, I think we have done a remarkable job on the acquisition of Aruba, which I did in 2015. Just to give a sense, when I acquired Aruba, it was mainly a wireless company, a Wi-Fi company, and the size of the business in 2015 was $750 million. I reverse integrated our campus switching because Aruba is a campus and branch-focused company. And combined, they were $1.6 billion. At the end of 2023, we were $5.3 billion. So we did a great job in -- more than 2.5x if you will, almost 3x the revenue of that business. And -- but you said it, right, obviously, COVID drove demand for modernizing the campuses, people came back to offices and the like. But now we believe we have passed the trough. And last quarter, we saw sequential orders and revenue growth, which is an important signal that the market is in recovery, and we expect that to continue for the next several quarters. And so that's another positive momentum we think we're going to have as we enter 2025, in addition to the fact that we expect to close the Juniper transaction by the end of calendar 2024 or early calendar 2025. And that will give us, for the first time in the history of the company, and I will say, even the history of HPE, if you go back, a full intellectual property stack from the silicon, because our campus switching is 90% our own silicon, 100% of it. So from the silicon to the infrastructure, to the operating system, to the software, the security and the services, to cover the edge-to-cloud spectrum through this modernization that we need because of AI, in an AI-driven approach for the $180 billion TAM, which basically, we can cover the entire TAM. And remember, the combination of these 2 companies will be an $11 billion business, so more than double the current networking segment as reported, and it's going to be representing 35% or so of the company's revenue or more than 50% of the company's profit. So clearly, we are shifting this -- the look of our portfolio to higher growth, higher margin areas on a control point that's essential as we go through this inflection point from the architecture perspective. So we feel good. The momentum is there. Recovery in the market, in line with the peers, by the way. The Juniper transaction closing around that time line, I just said. And the combination of the 2 assets giving us a different financial profile. But most importantly, a different IP that give us the relevance in this new market.

Right. And Intelligent Edge is one of the higher-margin segments within the organization, mid-20s long-term EBIT margins. Could you discuss some of the factors that are going to impact margins over the near to midterm within Intelligent Edge? Why is mid-20s the right long-term margin profile?

A couple of things. As I said, at the hardware level, we own much of our IP except the radio access piece of it, which obviously we work with Broadcom and Qualcomm. But on the other stuff, on the campus switching, it's our own IP. But a lot of the momentum we see is the experience. Aruba was conceived with a cloud-native approach in a mobile-first approach, which allows customers not only to provide connectivity but to build experiences around that connectivity. And we see tremendous traction in each of the verticals, whether you take hospitality or whether you take health care or whether you take manufacturing into the IoT space. And we have been very diligent in adding to adjust its market along the way. So again, Aruba was a Wi-Fi, reverse integrated in campus switching, then we added SD-WAN through the acquisition of Silver Peak. SASE is a necessity at the edge, through the SSE, the secured service edge. So now we have the true consolidation of the network layer. We expanded into private 5G, which is a huge demand for manufacturing and other verticals, and [ intro to ] IoT. So we believe we have a full comprehensive approach there. We added, on top of that, the cloud layer, and that's one of the reasons why Aruba is growing so much inside HPE GreenLake, and our ARR is $1.7 billion and growing close to 40%. And then obviously, the AIOps, which is this AI driven. But Juniper brings the complementarity with Mist in one segment of the market in the campus and branch and then into the cloud and the service provider space and in the enterprise data center switching space.

Yes. On capital allocation, HPE put out a target to return 65% to 75% of free cash flow to shareholders over the next few years. Can you talk about how you're balancing the return of cash to shareholders with reinvesting in the business and M&A? And what's your appetite for more M&A, particularly given the backdrop of some of the pending deals?

Yes. So first of all, we always return capital to shareholders. I think about the -- I've been 6.5 years as a CEO. In the first 6 years or so, we returned almost $11 billion of capital to shareholders. That's -- when you think about our free cash flow, approximately around $2 billion, right? That's in line to generation of cash, although we had obviously the spinoff at the time before I became CEO. That said, obviously, the first thing is to pay dividends. And dividend is a very critical component of our capital allocation. This past quarter, we paid $0.13. At the beginning of the year, we raised the dividend by 8%, so that's consistent. In terms of share buybacks, it's an allocation between share buybacks and investment where it makes sense. We do that on a stringent return on invested capital. And also, understand we need to make investments in the business. And we felt that this time, doubling the networking business was the right time because of what we see in AI and because of the ability to change dramatically our financial profile. And by the way, that transaction is a no-brainer for shareholder because in the end, we are committed at least $450 million of synergies, which pays for the deal itself, so I believe that's a no-brainer. But obviously, as we go forward, our goal is to deliver that 65% to 75%. And the first thing is that in year one post-close, the transaction is already accretive on a non-GAAP basis. Obviously, we're going to drive the synergies as aggressive as we can. But already, year one post-close, this is going to be accretive for shareholders on a non-GAAP basis. And then obviously, pay down the debt, which our commitment is to bring it down to 2x EBITDA. And we have plans to do that. And so that's the journey we are going to be. And honestly, as ultimately, this company had done what we said, and I'm very confident in our team to go execute the strategy.

Antonio, it's been such a privilege to have you on stage. Thank you for joining us here at the conference.

Thank you, Michael.

Thank you.

All right.