NetApp, Inc. (NTAP) Earnings Call Transcript & Summary

Hello, and welcome to today's webcast titled, "The AI Era." I'm Russell Fishman, Senior Director of Product Management for NetApp Solutions, and I'll be your host for the session today. We have an awesome agenda that I believe you'll find extremely valuable in continuing to grow your understanding of how the most successful organizations are adopting AI and what lessons you can apply to your own journeys. To take us through this, I'm extremely privileged to be joined by experts from a number of leading AI companies, including NetApp, NVIDIA and IDC. With me today are Jonsi Stefansson, Senior Vice President and Chief Technical Officer at NetApp; Ritu Jyoti, Group Vice President of Worldwide AI and Automation Research at IDC; and Tony Paikeday, Senior Director of Marketing at NVIDIA. Ritu and Jonsi will discuss results and key takeaways from IDC's recent AI transformation study, then Tony and I will then spend some time covering our latest joint solutions announcements, and we'll wrap up with some Q&A. So let me start by introducing the study and associated report that we'll be discussing today, scaling AI initiatives responsibly, the critical role of an intelligent data infrastructure. To scale AI initiatives responsibly, organizations need an intelligent data infrastructure. This intelligent data infrastructure must have the flexibility to access any data anywhere with active data management to enable superior data security, protection and governance and adaptive operations to maximize performance and efficiency of their infrastructure and applications, all while optimizing cost and sustainability. Taken together, these can maximize AI knowledge work and productivity and propel organizations to more consistent success as they use AI to achieve more for their businesses. With that, let me hand over to Ritu and Jonsi to take us through the key takeaways from the report.

Thank you, Russell. It's wonderful to be a part of this discussion today. We all have witnessed Cambrian explosion of generative AI technologies in the last 12 to 18 months. And today, we can easily say that generative AI is poised to be one of the greatest disruptors to impact business and society. With all these exponential changes in technology, a business environment seems like metaphorically, a fluid unpredictable rating portion. In fact, as per IDC Research, 37.4% of the respondents have reported that generative AI will disrupt the competitive position. So that's the question that what exactly is happening? And who would have answered it better than Larry Summers, I love his response. He says AI is coming for the cognitive class, and there is a substantial chance that AI will be a threat to IQ versus EQ. So essentially, what he is saying is that this is going to impact every knowledge worker and thereby every organization on this planet. We at IDC, as per our AI spending guide, we are predicting that the worldwide AI spend will exceed $512 billion, and GenAI is a critical catalyst to drive this exponential growth. And what we are seeing is that the use of AI machine learning is occurring in a wide range of solutions and applications, be it your ERP solutions, the manufacturing software, content management, collaboration, end-user productivity. And it's critical to note is that IDC expects that AI will be the most destructive influence changing entire industries over the next decade and for future. But the most interesting point that I'd like to zero on in is that the changing landscape of generative AI market opportunity. As I noted, GenAI is going to be a big catalyst for the overall AI adoption, but when you kind of take a look into the finer details as to how the GenAI market is evolving, in the earlier days in 2023 and 2024, a huge chunk of investment is going towards infrastructure, but the real meat and the value that an end user will be able to kind of accomplish will drive the forecast towards apps, platforms and services. So given all of these factors that are going in the industry, in December of 2023 and January of 2024, IDC conducted both qualitative and quantitative research with global AI decision makers to assess the state of AI initiatives at their organizations. And as far as the study, we actually looked into 51 distinct inputs to assess the maturity of the organizations, be it AI strategy, whether they're kind of ready from a data perspective or whether they are ready from the infrastructure perspective, and it's the storage latency, the scalability, the access, the movement of the data from a distributed environment or whether it's data governance and related processes, security, as you and I know. We all -- that security is top of everyone's mind. And so whether it is data security or data privacy, how efficient are they handling their data sets. Cost. Cost is the number one [ inhibitor ]. So we also kind of booked around it in terms of cost efficiencies, storage optimization, storage rightsizing, processes and tooling for data science as well as developer productivity. And as part of this study, at the end of it, we actually classified them into 4 different stages of maturity. As you can see on the slide, right from AI emerging to AI Masters. And our assessment kind of gave us a very interesting insight that it's kind of equally split between AI Emergents and AI pioneers. When you combine the two, they almost 50:50 split, but there's a very small percentage of the organizations who are at the master stage. So as we kind of start looking into what are the reasons the AI failures are caused, and the number one reason, be it an AI for an AI Emergent or an AI Master, it's actually [Audio Gap].

Sorry, closed due to the technical difficulties. Getting back up and running now. [Technical Difficulty].

[Audio Gap] you can see that the percentage of improvements needed, whether it is minor or major or complete overhaul, there's a stock contrast between the Masters and the Emergents. And the point that we were trying to stress on is like the Masters have been on this journey for a longer division of time, and we always hearing from the end user that those who have been on the AI journey, especially some of the organizations and financial services or retail or manufacturing and health care, they are better prepared to kind of harp on to the game-changing transformative opportunities with GenAI. So as you can see from this particular chart, there is some very interesting insight that organizations are not dealing the data, which is stored in one particular location, it is actually distributed across different data locations, be it on cloud, it can be different types of data, and especially hybrid and private cloud scenarios kind of shape up. So ability to integrate the organization's private data with the data that is stored on public cloud, seamlessly integrate and especially in the case of when we are talking about GenAI, organizations are working towards contextualizing their data with their own private data and the technique of RAG is really, really front and center for all of that. In all of these cases, the underlying data architecture readiness is critical. I would love to hear from Jonsi, I'd like to invite him to get his perspective on what he is hearing from his customers and his point of view from a NetApp perspective.

Yes, I fundamentally believe that AI workload is going to stay hybrid forever. Companies will have to be able to freely migrate and replicate data to the public cloud or a service provider of choice when it's needed. That's why we've actually put a lot of work in integrating our first-party storage offerings with native services in the public clouds like Vertex AI in Google, like SageMaker and Bedrock in AWS, like Azure AI services for Azure, of course. And we have our own AI toolkit that allows you to basically chat directly with your data. And honestly, nobody does data management like NetApp. And that's the number one reason why all 3 of the hyperscalers chose NetApp as the world's first external native storage offering in the public clouds. It's for the data management capabilities and ease of migration. And our integrations with NVIDIA NeMo Retriever is basically RAG enabling every single deployment of ONTAP, whether it's on-premise or our first-party storage offerings with the hyperscalers. So you can really take proper advantage of your hybrid cloud or multi-cloud strategy for AI. I mean, ultimately, your trained model is only as good as your data.

Yes. Thank you, Jonsi, I couldn't agree more on that because there's no AI without data. And many organizations that I speak to, they kind of complain about that they have been working on data issues for the last decade or so, but it's still an ongoing problem, and it becomes really, really critical in the age of AI, but AI is becoming more mainstream. So with that, let's go to the next slide. So the second imperative that we are kind of looking at for an organization to scale AI initiatives responsibly is to actually become responsible at the core. Every organization needs to define their principles that they need to adhere to, they need to have an AI governance committee that can actually ensure that they're being adhered to. And also every individual in the organization needs to be educated and made aware of what the critical requirements or principles are. In addition to that, they need to have the right sets of tools and technologies because responsible AI is not just at one part of the stack. It kind of does to the entire AI life cycle, right from the infrastructure to the model, to the application and the end user layers, every organization needs to understand that this is not a destination but a journey, and they need to kind of continue to learn from their experiences, iterate upon it and continue to enhance and improve their responsible adherence. So when we kind of dug further into the insights that we got from the survey that we are talking about here, one thing that stood out for us is that if you take a look at this right side of the chart here, that the standardized policies are in place and rigorously enforced by independent group within the organization. And I want to underline that rigorously enforced, it's not enough to just have policies and principles, but also kind of the right set of tools and technologies to kind of observe it and do this on an ongoing process. That's a stark contrast between what AI Masters have and in terms of AI Emergents. If you look at the AI Emergents, they are still in the process of [ docking ] policies and procedures are being developed, but they don't have the standardized processes to put them into enforcement. And that's the key difference. So all policies must be standardized and also constantly observed and monitored and improved upon. And when you look into some of the critical aspects, when we talk about data security and privacy, because it's really front and center and we started the conversation about that most of the failures are happening because of the data issues. And I set the stage on how organizations need to become responsible at the core. The important aspects to be looked into via AI Master versus AI Emergent, they are very more advanced in terms of mitigating bias or data sovereignty concerns and the likewise when it comes to data security and privacy. So let me spend a couple of minutes on talking about what are the best practices that the AI Masters have figured out vis-a-vis the AI Emergents. So essentially, first of all, when you're talking about bias or data sovereignty, they really need to know and put in the right set of data, prepare the data. Depending upon the use case that they are working for, they use the right set of tools and technologies to create the right balance of the data. They might be used synthetic data in some cases, and there are some data sovereignty rules. Federated learning could be put into place so that you do not really kind of move the data outside the geographies. When it comes to data security and privacy, it's extremely critical, especially in the RAG GenAI. Everyone's talking about your data is your data. It should not be in kind of an IP leakage. Organizations should be asked for their permission if they're willing to kind of share their data to kind of use it for further training of the model. So those checks and balances are very strictly adhere to, and we are making sure that all the regulatory policies are kind of also included. If you recall the slide that I was sharing about responsible at the core, that's what I was talking about because the regulatory policies are evolving. They really have the right sets of tools and technologies to make sure that these are in place. But at the same time, they also kind of making sure that they are partnering with the right technology suppliers. And these technology suppliers need to have the right responsible principles and the responsible guardrails in order to give them the sense of confidence.

There is a balance between speed and good governance. In the long run, [indiscernible] and knowing your data, we set you free to innovate and succeed with AI. Your data today is the #1 attack surface for hackers, and we are constantly innovating in this space and being able to train our own models to essentially monitor itself and have an automatic reaction to any abnormalities is the only way to go and can only be achieved with AI, because a human is highly unlikely to be able to catch it in time, let alone fix it before the damage is done. And that's why we are very confident in offering our ransomware guarantee to our customer. And the other thing is you really need to map out your data, and that's the key aspect of being able to take advantage of your AI hybrid or multi-cloud strategy. You might have PIA, HIPAA, PCI or GDPR data that can't just be uploaded to any LLM. What needs to be [indiscernible] before or tracked before and so on and so forth. So I mean, the only way to really speed up your AI journey or your AI advancements is to know your data, mark your data and secure your data.

Well said, Jonsi. I think all these tools are really, really going to be game-changing in enabling the efficiency, the acceleration of time to market. And organizations, especially the Masters, they are very more advanced in working with superior solutions provided by folks like you all. So with that, next slide, please. So now we come to the next -- the third imperative for organizations to skilled area initiatives, and that is all about resource efficiency. So as AI workflows become increasingly integral to the various industries, it is important for us to acknowledge the implication on compute and storage infrastructure, data and energy resources and their associated costs. Here, we have a very interesting insight from IDC Research, where we are looking into that the AI data center energy consumption, they will grow at 44.75% CAGR from 23 terawatt hours in 2022 to 146 terawatt hours in 2027, and that's pretty alarming. We really need to come together as an industry to look into what are the ways and how we can do better optimization and better utilization of the resources. We are not saying that you need to limit the user GPU, but we have to consider the most resource-efficient technique for your use case and make brand-market choices. We also have to look into better management of the infrastructure needed to support AI and GenAI and very focusing on timely business outcomes, but then we have to look into how do we do the cost optimization, end-to-end performance optimization, energy utilization while not forgetting the scarcity of GPUs currently. So when you think about resource efficiency metrics that needs to be standardized, when we asked this particular survey that how they are kind of looking into whether they have clearly defined metrics for assessing the efficiency of resource, the answer is very, very startling because if you think about the Masters, they have completed and standardized it across AI projects, whereas the AI Emergents, only 9% of them are completed. And in terms of not starting yet, the number is very, very high. And that's why we are calling out and sharing that these best practices from the AI Masters is to how they can actually look into the -- first of all, you always you can monitor something, you can get better at something only when you start kind of recording this to what really are the important metrics that you have to go after. And that difference can be clearly seen here between the AI Emergents and AI Master. So I'd like to invite Jonsi for his comments on what he's hearing from his side of the house.

The report actually calls it out just like every single customer meeting, I mean every AI is on the -- is the top of the agenda for any meeting you go to these days. The cloud conferences are no longer cloud conferences, they are AI conferences. And it was amazing to see NVIDIA GTC, and you met basically everybody in the industry there. But the funny thing is everybody seems to be sort of that set on creating more silos instead of going for standardizations. They should be able to use the same technology, same data management capabilities as for other workloads. I mean, for example, you can't just sacrifice your standards, your governance, your compliance for just speed. Speed alone is not going to make you go faster. What makes you go faster is knowing your environment, having standards like the Masters do. And this is where the Emergents are actually struggling, and we see that with companies that we are helping get them started. So from my perspective, standardization is key. And another important thing is the data unification. Choose a vendor that can offer you all the storage protocols, use for any workloads, not just AI. I mean having block, file, object, all managed by a single pane of glass or all having the same -- and being able to access different, like file and object duality is key for integrations with all the analogs platforms out there because you can't just expect them to this only works for file. This is only -- you only need block here and object is key for [indiscernible]. Like NetApp helps our customers become the data pipeline. The data pipeline for AI allows you for different phases within your AI journey, you need different performance metrics or characteristics. So we can basically call and move your data to high-performance controllers when it's needed and move it to a cost-efficient storage when you don't need to be training or fine-tuning your model or like NeMo Retriever, you can actually do like just in place. So like collaborating and working with ISVs that are all in this space is also a key. You need to be able to play with everybody basically, but don't sacrifice your standards.

Yes. I couldn't agree more with you on that, Jonsi, because it's -- when I speak to the end users, they typically tell me that they have been spending so much time in kind of moving things and incomplete connections between the different disparate systems, and that's really, really kind of slowing them down. So very well said. Building on to this resource efficiency topic we are just kind of talking about right now, it's very, very critical that we actually have built [ by tune ] kind of a discussion as well because this has a critical impact on your resource efficiency. Being in the industry for a long time and watching this whole AI, the traditional AI explosion, I've seen that intuitively, many organizations to take a lot of pride in going and building things themselves, where the market is kind of rapidly evolving the pace of innovation in GenAI, it's -- could be impossible if organizations think about doing build for everything. So make sure that you actually have the right set of decision-making and its impact on what kind of decision you're making, whether it's on the return of investment, do you really have the right skill set, do you have the right data sets, do you have the right resources, is it really kind of a game changing, truly differentiated, or is it a nice to have kind of a scenario, do you have the area where you can think about whether you're going to really move the needle extensively by doing something by building yourself? But in majority of the cases, you might be good enough by fine-tuning or contextualizing it and automated prompt engineering. But there are scenarios in which you kind of take something off the shelf and actually tune it or to kind of better cater to your unique needs, so please keep in mind that this decision will actually have a direct impact on your resource efficiencies as well to try to simplify the decision. So betting on to that, it's important to kind of think about that when you're looking into all of these decisions, you really have to focus on improvement. What kind of improvements do you need to get to the right sites storage for AI, right? It could be that consider the storage that can keep your GPUs utilized and first into cloud for GPU access because we've been talking about hybrid scenario to be very, very kind of prevalent. You could be thinking of looking into storage that can be data and energy efficient. It can unify data and reduce data silos. That is one of the biggest problems that I hear from all the end users and also develop best practices around mitigating the -- what [ peers ] of storage you are using, how you're kind of seamlessly moving it, where applicable you can use federated learning with all the right data security and privacy requirements. So it has a lot of interesting insights as to how you can actually optimize your storage to drive better resource efficiency for AI initiatives. I would love to hear from you, Jonsi, as to what are you hearing from your side of the house?

Of course, we optimize our storage for NVIDIA GPUs, and we are laser focused on maximizing that performance and utilization for our customers that -- our joint customers. But I mean, ultimately, like I said earlier, fundamentally, AI workloads are going to be hybrid. And for model training, some customers just simply don't have the power budget to support model training in their own data centers or even potentially don't have the need the GPUs available for that particular task. So you need to be able to burst and connect into like NVIDIA GTX cloud. And that's sort of the design principle of the concept that we call intelligent data infrastructure to be everywhere where data lives, protect and securely deploy data wherever the customer wants it to be. We are constantly striving for sustainability, less power consumption for our controllers, less carbon footprint. Data or storage efficiency within our own portfolio is of super important, like compression, deduplication and all of that. That all lowers the -- or increases the efficiency. And we'll do our part to facilitate or to get AI done as efficiently, considering performance, cost and sustainability. That's part of our mission statement.

Thank you, Jonsi. And as we can all agree with this that the rightsizing will also mean a reduction in data silos and incorporation of very comprehensive hybrid, unified and multi-cloud storage approaches and giving the organizations the flexibility that they need. So we have had such interesting discussions about all the different imperatives that an organization need to kind of scale their AI initiatives. I'd like to close with this very interesting data point here. So if you think about here that we are really interested. All the AI initiatives is all being driven to drive superior business outcomes and kind of organizations to unlock their defensible modes and continue to thrive. When you're thinking about this, if you take a look at the data point here between AI Master and AI Emergents, AI Master, they experienced the lowest 12-month improvement in business outcomes. So if that's the question that why is it? The reason is because they have accumulated benefits from prior stages of their journey. They are working in way more complex initiatives. So it can be a little bit kind of confusing, but it's -- they're still experiencing significant, significant improvements, they are on up for long journey, whereas the AI Emergents, they are kind of just starting on their journeys and they are experiencing the greatest 12-month improvement in business outcomes because they are looking at much more simpler, but also they don't have that accumulated benefits from their prior stages of the journey. So it can be a bit misleading, but the really -- details are needs to be focused upon as to what differentiates the AI Emergent versus AI Masters. So in closing, I'd like to kind of stress on the path that AI is not a nice to have, it's not a choice or it's not an option. Every organization who is at an AI Emergent, they really need to kind of accelerate their journey towards being an AI Master. We have some very interesting insights and guidance based on the discussions that I have with Jonsi right now as well as a part of our detailed study that we'll be sharing with you all. So take a look at that and we'll encourage you all to kind of engage with all of us to learn further. So thank you, everyone. It was wonderful to discuss our insights from our collective study. And with that, I'm going to pass it over to Russell.

Wow, those were some great and actionable insights. My sincere thanks to both Ritu and Jonsi for that heat into what we can learn, in particular, from best practices of more AI Masters leading the charge in this era of AI. As Ritu mentioned, the report that we've been discussing is available today through netapp.com. And for those of you who registered to the webcast, you'll get an e-mail with a link to the report too. But before I move on, I wanted to take a minute to summarize what we have been talking about today in the fundamental and critical role that data plays in helping organizations scale AI to the whole enterprise. Let's start with the reality that data is everywhere. It doesn't confine itself to traditional definitions of on-premises or cloud. It's generated everywhere and is needed anywhere. That's why we believe organizations that want to accelerate their success in AI, either flexible data and storage architecture that is at its core seamless, hybrid and multi-cloud. And data can be held back by the proliferation of so-called specialized storage and data silos, which add hugely to the complexity of making AI real. We believe the future of AI will be delivered by a unified data environment that is optimized for AI throughout its life cycle. Organizations are leveraging their most precious data to drive this AI revolution. This data isn't just subject to legal and regulatory concerns, but it's also commercially very sensitive intellectual property and yet, the very folks that are charged with innovating at such an incredible pace around AI such as the data scientists and data engineers aren't typically that focused on the responsible use of this data. That's why we believe that organizations must demand an intelligent data infrastructure that delivers built-in governance security, modeling data traceability and provides automatic defense against malicious attacks. AI requires these incredible resource needs. So although it may seem like table stakes, having a unified data environment that continuously deliver the performance required that any AI workload is critical, all while ensuring it's being done efficiently from both cost and environmental perspective. Finally, what uses a unified intelligent data infrastructure if it doesn't directly help the users that are actually tasked with working together to deliver AI. That's why we believe in a data environment that is tightly integrated with MLOps platforms that are used daily to innovate in AI, focused on delivering improved productivity and maximizing the use of these hard-to-find roles. Now with that, let's switch gears and talk about our partnership with NVIDIA. We've just come out of the NVIDIA GTC event in March. And together, NVIDIA and NetApp made a number of exciting joint announcements, which we're going to recap now. First up is this great quote from Jensen Huang, the CEO of NVIDIA. He talks about one of the main reasons why we continue to expand our long-standing, more than 6-year relationship around AI. Data is the field that is driving this current explosion of generative AI solutions. And NetApp's leadership in intelligent data infrastructure for AI makes us a perfect complement to NVIDIA and their incredible rate of innovation in the space. In particular, our unstructured file data capabilities are foundational to solutions like, retrieval augmented generation, or RAG, which was front and center at GTC. And to talk more about this, joining me from NVIDIA is Tony Paikeday, Senior Director of Marketing. Tony?

Thanks, Russell. So I've had the privilege of partnering with NetApp for, I'd say, the last 8 years, beginning with the creation of our first integrated infrastructure solutions for enterprises. And since that time, our two companies have embarked on the journey of taking something that used to be exclusively the realm of maybe science and academia. I mean no offense to either of those. And democratizing AI for every enterprise, developer and data scientists who needed a new better platform on which to innovate. So over the next few minutes, I'm excited that you and I get to share some of the most recent advancements in our joint portfolio, is unveiled at the GTC Conference.

Thanks, Tony. Yes, so let's get on to that recap of those GTC announcements. First up, I wanted to cover the new NetApp AIPod. So leveraging the NVIDIA DGX BasePOD architecture and the DGX SuperPOD product, NetApp AIPod is a set of reference architectures designed to help our customers accelerate their adoption of a broad range of enterprise class AI training environments. AIPod builds on and is a combination of over 6 years of joint innovation between NetApp and NVIDIA. Combining NetApp's AFF A-Series and C-Series storage solutions with NVIDIA's DGX delivers the performance customers need, nondisruptive scalability and a unified approach to data management throughout the AI life cycle. Customers enjoy a simplified deployment and operational experience with built-in data security and threat protection. And with the previously announced NVIDIA DGX SuperPOD with NetApp E-Series, customers get the ultimate in speed and density of HPC and onshore performance workloads from the same partner they trust with their AI pipeline. So Tony, if you had to pick, what are the three most important things you're hearing from your customers when it comes to architectures purpose built for AI?

No, Russell, this is why I'm so excited about our partnership. Our two companies are single-minded in our focus to help businesses overcome the challenges of the AI platforms. Customers repeatedly tell us, first, help me eliminate the complexity of designing infrastructure and navigating that delicate balance of compute, storage, networking, software, all working together as a full stack solution. I guess the second thing is putting all the componentry together can be hard. Deployment time frames get elongated, delaying the point at which one's developers can actually be productive. So they're saying give me a faster way to deploy backed by partners who have the competency to do it all. I guess the third thing is driving performance and higher utilization of infrastructure can seem like a black box to those who don't have a deep bench of HPC expertise. So they say, "Give me solutions that offer linearly predictable performance that scales, that's IT manageable, that's backed by enterprise-grade support and AI experts who can help solve problems." So we're excited about AIPod because it addresses all three of these concerns.

Appreciate those insights, Tony. Those that were able to catch the keynote, NVIDIA GTC may have seen this picture on the main screen behind Jensen. We are super excited about highlighting the work NetApp has done to demonstrate how to integrate our ONTAP storage OS into the recently announced NVIDIA NeMo Retriever. Building on our strength in the enterprise data center, we've enabled our customers to simply and rapidly integrate their existing unstructured file data into a generative AI knowledge management solution powered by the NVIDIA AI enterprise software suite. We talk about helping our customers talk to their data. With this solution, we're accelerating our joint customers' journey to extract latent value from that data. So Tony, how do you see retrievable augmented generation impacting the GenAI landscape in our customers?

Wrestled for years, we've partnered on helping leading edge companies train mission-critical models from scratch, and we've done really well at it. But we're entering into this new era of AI because there's a couple of things. First, rather than serving up answers and content that already exists, the future of AI is generative, creating answers and content that really never existed before using natural language as the API for this capability. I guess the second thing is with the advent of RAG, as you pointed out, we're now enabling businesses to tap into oceans of unstructured data they already have and literally speak with their data. Talking with their [indiscernible] PDF files, technical documents, customer transcripts, financial reports, you name it. So who better to help them unlock this capability than the partner that they already trust with their data, namely NetApp. So unlike the paradigm in the last decade, AI is no longer confined to those who are committed to creating their own foundation model from scratch every so often. Now literally anybody can get incredibly accurate up to the answers, leveraging data that they're already sitting on, unlocked by this joint solution leveraging NVIDIA NeMo Retriever and NetApp.

I agree, Tony. We see this as -- I think we both see this as truly revolutionary rather than just the evolutionary. So now let's turn our attention to how NVIDIA and NetApp are working together to help organizations operationalize their AI workloads at scale. NVIDIA recently announced their updated OVX platform, optimized for the enterprise. It targets key functions, choose inferencing, fine-tuning and [ might ] training at scale is ideal for workloads, including generative AI and techniques such as RAG as we just discussed. NetApp's intelligent data infrastructure capabilities and enterprise pedigree combined with holistic support for the entirety of the AI data life cycle means that we are perfectly suited to NVIDIA OVX. And we recently announced a NetApp ONTAP is certified as a storage partner for the OVX platform. So Tony, we are super excited about partnering with NVIDIA and OVX. Can you share how it complements the rest of an organization's end-to-end AI solution?

Yes, Russell, we really see two modes of AI development emerging in the enterprise. I mean there's the training and customization of foundation models that require massive computational power to deliver a production-ready model, maybe in hours or days instead of what used to be like weeks or months. But additionally, we see the rise of RAGs, as we discussed, leveraging pretrained models, working in tandem with an embedding model and vector database to augment those models with live enterprise data for more timely and accurate answers. The second mode requires a platform that can live where the data is created, maybe at the department or at the business unit level or even the edge of the enterprise, alleviating the pressure of having to continually retrain a large or very large model. The OVX infrastructure fills this need with a platform that can be deployed cost effectively where the data lives, optimized for fine-tuning and RAG deployment with NetApp intelligent data infrastructure, enabling effortless mobility of data sets and models to wherever they're leveraged.

Thanks, Tony. Yes, we certainly see this huge drive towards operationalization. It's the next big frontier. It's really where we're going to start saying folks generate real value out of their AI investments.

So we're now going to move on to the Q&A section. So participants, as mentioned earlier in the session, please go ahead, ask your questions using the Q&A feature in Zoom. I'll go ahead and pick them up to ask the presenters. And for the questions, I'm pleased to say that we are also being joined by Will Vick, who is the Global Director of Technical Sales and Strategy, NVIDIA. So let's see what questions we have for the panel. Okay. So let's start with this one. Can you explain the definition of unified storage used in the study and its relationship to accelerate time to business value. Let's -- Jonsi, would you be okay taking that one?

Can you repeat it? I just broke up a little bit.

Yes. So can you explain the definition of unified storage used in the study and its relationship to accelerate time to business value?