NetApp Inc AI Tech Talk Hosted by Evercore ISI Transcript

The following is excerpted from the question-and-answer section of the transcript.

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : Perfect. Thank you, Kris. I guess, Russell. So maybe just to start off with white, I'll spend a few minutes some of the history of where your NetApp has played when it comes to AI and what has that been working on over the past, call it, five, six years around the speed of AI development and really into the business, just to understand kind of the history of the company were on the AI slide? And then what do you see that going as you look forward?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : Perfect. And it's a great point, which is this is when we suddenly get more wider in terms of deployment beyond just the four types of hyperscalers. I think one of the questions I get asked, I'm sure a lot of folks knew from your asset there, but which is, you know, what is quote and unquote AI opportunity for NetApp going forward price, how do you kind of put dimensions around that would be very helpful. And maybe as you answer without the prior written consent of Thomson Reuters. 'Thomson Reuters' and the Thomson Reuters logo are registered trademarks of Thomson Reuters and its affiliated companies. JULY 10, 2024 / 8:00PM, EVR.N - NetApp Inc AI Tech Talk Hosted by Evercore ISI that or look, I understand that in three buckets, if you may, which is, you know, one kind of the incumbent installed base, how does that kind of backup? Secondly, the net new infrastructure built. And then third, there's a vast amount of data that actually needs to be prepped for enterprises for the AI use cases, right? Maybe just talk about what is the AI opportunity? How do you size that up and that as you go forward?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : That's waiving. Yeah, I do want to spend a little bit of time just on RAG and Persio that means we focus it goes forward. But when you think about some of these capacity requirements, if you may, let's understanding of when do you expect to start seeing benefit from the growing data capacity requirements that's only AI model time?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : Got it. Just think about all the time how should we think about the time line of how data is stored through the AI lifecycle in its entirety, right? Like what happened to new data that's created a touch a little bit on how you count compressors and everything else, but will there be different performance requirements depending on where you are in the lifecycle be that training, tuning or RAG intrinsic, for example?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : Got it. I guess this would be good time there. You've also talked a little bit about what do you see the competitive landscape right now in your release talked about there are a lot of companies that are, I guess, you know, lack of a better word AI watching the narrative out there. I've looked and said, who do you see really are in these customer? Do you have the junior customers? Who else are they looking for RFPs, for example, and maybe also contract, how has the landscape changed from a software perspective people?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : I have a question. A couple of questions from folks on the call here. One of them is just on the competitive landscape. Is there a different set of customers or different sort of competition you see in the cloud side versus hybrid? It's really a question of the intended around vast data systems and where you see them play versus not. And we will start with that and there's one more after that.

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : And you just -- talking of NVIDIA, hoping you could just expand on your relationship with NVIDIA. Maybe you can frame it around what enabled NetApp to get SuperPOD certified earlier than some of your public peers -- at least their comparable products. And maybe related to that you can just talk about what do you think differentiates NetApp SuperPOD versus offering some -- your competition right now?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : I guess, when it comes to enterprises looking to deploy AI, what environment do you think they'll deploy AI as they go forward. Is it more on the cloud? Is it on the colo? And then maybe related to that, what environment do you think is NetApp best optimizated for?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : I guess maybe I'll ask you something in the questions like -- why is NetApp able to benefit regardless of where the data resides? That's a pretty unique proposition for you folks. And maybe just talk about why that's the case, and how is this differentiated and resonate with your customers? I think Lockheed Martin, for example, is building a center of excellence using NetApp. So maybe just talk about why is NetApp able to benefit regardless of where the data stored and how that resonated with customers like Lockheed Martin?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : That's really helpful. And then, one of the big differentiators you always thought about NetApp has been -- at least against your peers -- is this post body integrated position that you have across all three big cloud service providers. How is that proving out to be a differentiator as enterprises look to deploy AI? And then we can also talk about -- what are you folks doing specifically with these hyperscalers to integrate with their own AI offerings that each of them have right now?

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : I know we're coming up on time, but I have a question from one of the folks on the group here. So I'll ask that. Can you talk about what is driving the willingness of enterprises to transform the existing data into vector embedding? Maybe the spirit of this is, why would a enterprise take on the cost to transform existing data into this vector, and that is right now? Maybe you can just touch on that dynamic a bit.

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : I mean it's been four years of doing this. You'd think this will become a lot smoother right now; this is not. I'll just say I know we're coming up on our time, but maybe I'll squeeze one more question in before I turn this back to you folks. Maybe to talk about -- how is your go-to-market strategy evolving to capture the opportunity that comes to enterprises and AI deployment? How different is the AI deal from RFP deployment process? What sort of traditional -- we'll just talk about that go-to-market stuff a little bit.

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : Perfect. I think with that, maybe, I'll stop my questions. I'll turn the virtual mic back to you, Kris, Russell, Hoseb. Hoseb, do you see there any closing comments? Anything we did not touch on that -- and if you want to flag our way as you think about AI and NetApp's value proposition there. without the prior written consent of Thomson Reuters. 'Thomson Reuters' and the Thomson Reuters logo are registered trademarks of Thomson Reuters and its affiliated companies. JULY 10, 2024 / 8:00PM, EVR.N - NetApp Inc AI Tech Talk Hosted by Evercore ISI

Question: Amit Daryanani - Evercore ISI Institutional Equities - Analyst : Perfect. Thanks a lot. It was a pleasure, and thanks for your time for Hoseb, Russell, and Kris. We'll chat soon. Thank you, everyone.