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

That was going to be my follow-up. So I know it's not your purview in terms of helping them design or innovate their applications. But when you brought in a discussion from the CloudOps side, is it as the innovation is happening? Or is it a customer says to you, hey, we've got this great application. We're working on this workload. Do you think we should develop it in the cloud? Do we develop it on-prem? Or is it more of a secondary consideration at some point in the journey effectively from a customer's perspective?

So we see customers, I think, at all stages. So it can be, we're doing something new, help us understand what the deployment challenges might be. They may have a strategy in mind. So I'm thinking about a customer that we're working with at the moment that is looking to make a move from a hyperscaler-deployed environment back to an in-house one. And they're giving all of that consideration around TCO. And so there's an engaged process in that and having a discussion with the customer about all of the benefits of doing so and what that cost will be and how that falls out and what the opportunities are to optimize. And my experience working with our customers has been that, as Ronen was saying, optimization doesn't necessarily happen out of the box either. It can often be a peak followed by some optimization, followed by another peak, followed by some future optimization as features and capabilities evolve. And -- but the whole goal of the trend over time is towards optimization.

And I think that workload deployment in the cloud is not a onetime opportunity. Even if you arrived in a late stage of the current workload, the next one is just around the corner, right? So you can arrive -- if you show and explain the value, you'll be early in the next corner.

Exactly. I mean the other types of customers that we'd experienced from the plan and doing a project through to, my cluster is on fire, please help us now because it's a critical application, is another example of a customer where you're almost in rescue-type optimization to get that customer stable and then you bring them into the optimization discussion off the back of that.

All right. Sydney?

Sydney Ho with Deutsche Bank. Well, this is -- I'm going to toeing the line about financials. But the last earnings call, you did talk about the shift towards first-party storage services versus subscription. Is that simply the cyclical nature of the subscription business going up and down? Or is there a more strategic move that -- towards first-party storage, whether it's from NetApp or the customer perspective?

So I'll clarify our statements and then hand it over to Ronen. And basically, we said, we believe our emphasis and our biggest opportunity is around those first-party storage services. We see that as an absolute unique differentiator for the company, a massive opportunity, and that's where we're really putting the wood behind the arrow. Ronen?

Yes. I'll second what you said. I think if you look into our general -- into any customer at any stage, whether they were or were not NetApp customers, very high percentage of them have an existing relatively large commitment to the hyperscalers. Being one peer or a first-party offering means that you do not need -- there is no need for a new contract. There is no need for a new engagement. There is a need for the workload or the team that works on the workload to make the right choices when it comes to the infrastructure and this workload. It's a massive advantage. This advantage translates from a financial perspective to consumption because they don't need to sign a new agreement. They don't need to have a new commitment. They just need to start using this environment, and that usage is translating into consumption, and that consumption is what you see eventually in the financial report. I think what we're saying is that now that we have 3 hyperscalers with agreements like that and so on, we'll see the consumption really translating that into revenues and so on.

All right. Well, thanks, guys. I really appreciate your time. Thanks, everyone, for your great questions. I'll set you free to go talk to customers.

Yes.

Thank you.

Thanks, everybody.

Bye.

All right. Well, now I think is the part of the event that you guys have all been waiting for some actual real customers that you can hear from and what they're doing and what their big challenges are. So with that, I'd like to invite Anthony from OpenText and Phil from Lawrence Livermore National Labs to come on up stage. And I did that from memory so I greatly apologize. It's been a long day already. Hey, thank you so much. Hey, Phil, thank you so much. All right. Why don't you guys have a seat?

Thank you.

So we're going to hopefully get most of our questions from the audience. But I'm going to kick it off by just asking each of you to introduce yourselves, where you're from and kind of what your IT challenges and environment look like. So that might take a while, but it will give them some time to queue up some questions.

Sounds good. I'm Anthony Lloyd, I'm VP of Technology Services at OpenText. I manage all of the infrastructure and operations for corporate IT. That covers everything from data centers, cloud, network, telecom, storage and compute, end-user services, site support. Gosh. And there's more, the service desk, the operations center and pretty much anything that touches an application or an end user. So I'm responsible for corporate IT. So we have a line of demarcation between corporate IT and the commercial side of the business just because we protect all of the back-end systems, the HR systems, financial systems, things of that nature, the commercial side of the house really handles all of the customer-facing applications that we sell for revenue.

All right. Great. Phil, a little bit about you?

I'm Philip Adams. I'm CTO for the National Ignition Facility at Lawrence Livermore Lab. I'm responsible for all of the infrastructure, everything from the underpinnings that runs the control system to how we analyze and process data to being able to make sure that we have a 30-year scientific archive that is there available for our researchers and our visiting scientists. It's quite a bit of a task and challenge to be able to manage something that broad, that vast for requirement that is always changing.

All right. Well, and you guys are also doing some really cutting-edge innovation and technology. So that's got to be a pretty data-intensive environment. How do you think about setting up your IT environment to deal with the massive quantities of data that you must face?

We tried very hard to make sure that we looked at ourselves as not as a unique entity in the environment. It's very easy, especially for a national lab to say, okay, we're going to go off. We're so different and varied in our needs that we're going to go build something unique. And what we ended up doing is saying, look, we've got the same LEGO blocks that are available to almost everybody else. Let's take that innovation that has been done in the industry and assemble it in a unique way to be able to do low-latency operations for a control system that has a way of being able to provide life cycle management of data over time and pin that to our databases. And being able to leverage technology as it is, make sure that my team can spend more time helping the scientists rather than trying to uniquely innovate things that industry has already figured out how to do.

All right. Well, and Anthony, when we were talking last night, you were telling me about the massive M&A pipeline that you have to deal with and integrating all these different companies. Why don't you say a few words about some of those challenges?

Certainly. So at OpenText, we grow by acquisition. And because we grow by acquisition, we have the opportunity, and I'll use that term, of trying to figure out what the best solution is to integrate things that we may not always know about. We go through a due diligence exercise, but you don't always get the full picture until you get under the covers. So part of this is having flexibility not only to be able to integrate different technologies, different solutions from different sources from around the globe and be able to do that in a seamless manner that allows us to do it in a short period of time in a very secure manner and not have to recreate the wheel every time we do it. NetApp gives us a lot of those capabilities because we operate in all the hyperscalers. We have the ability to acquire and integrate anywhere in the world. And one of the beauties that we have is no matter what that environment is, we can have a solution from NetApp that allows us to get that done successfully and quickly.

All right. And Phil, how do you use NetApp?

We use a lot of NetApp technologies in our environment. We leverage FlexPod and AFF in order to get low-latency compute in our environment and low-latency access to data. We've leveraged FabricPool and StorageGRID behind our Oracle databases to be able to give a transparent life cycle, data life cycle management on that data set. We leverage stat manager and SnapVault in order to be able to do a comprehensive backup environment. So pretty much listed quite a bit of NetApp's portfolio of applications in the suite.

Definitely. And I see you nodding along. So it sounds like you're also using a pretty broad swath of our technologies.

Basically the same technology then add a few more. So we really rely a lot on ONTAP, Google Cloud Volumes. We use a lot of that technology because it allows us to quickly integrate different solutions and not have to go out of the box to figure out if we have to have a different way of doing things every time we encounter a different acquisition model.

I'm loving because I'm seeing you nod. So definitely, you guys are using so much of NetApp technology, you're forgetting the different names of the products. That's kind of exciting to me. All right. Well, let me turn to our audience and see if there are any questions. If you guys have questions about all these guys are utilizing technology, the challenges they face, it's your chance to ask real customers, real things, Steve?

So one of the hardest things to figure out is...

Say who you are for the webcast.

Steve Fox with Fox Advisors. So one of the hardest things to figure out as an outsider is how you become more efficient with storage. It's generally thought to be a consumable, but it seems like every cycle, you're able to consume more with less. So can you talk about maybe how you use NetApp to do that? And what that means for your infrastructure purchases now versus maybe 3 years ago?

Certainly. Well, what we've been doing is primarily, if it's in the cloud, obviously, we're using ONTAP or cloud volume. But if it's on-prem, we have various solutions depending on if it's a filer, if it's an A700, depending upon the performance and everything else associated with it. What we're doing now is really trying to move to more of a consumption-based model so that we don't have to make those CapEx investments. So we're really looking at keystone and looking at how we can use that as capacity on demand, whether we use it on-prem or whether we use it in the cloud that allows us to quickly deploy what we need, not just buying additional capacity, which you end up doing when you deploy on-prem, but you buy what you need and then you can quickly spin up additional capacity or downsize it as you need to, based on your needs. That gives us a great deal of flexibility, and it really helps us to manage our financials much more efficiently.

Phil, anything?

Yes, I'd say that the first order -- the National Ignition Facility is a research project where we're trying to study the phenomena of high-energy density science. So we capture all the data. We store all the data for 30 years because of just how hard it was to get the data in the first place. And so in a couple of ways that we've tried to be -- I think some of the -- what you were asking about is how are we thinking about reducing costs and whatnot. One was the life cycle management that we did on the data to reduce the total cost of ownership of storing that. Once we get a better understanding of the types of data that we need to and fuel our machine learning algorithms to be able to understand exactly the types of data that you really want to store, we can get a little bit more efficient in terms of being able to do localized processing nearest to the diagnostic end points, maybe then have that be a little bit more intelligent at that point, and only feed the data up that we really want to store long term. But right now, when we're in a cycle right now where everything is new, everything is amazing, you don't really know where the breakthroughs are going to be, you got to keep it all right now. And I think as time goes on, we're going to be a lot more efficient with the way how we do analysis and data. But yes, for the time being, it's all about how to reduce the cost to maximize taxpayer money.

And are you guys using the full suite of NetApp efficiency tools so that you're effectively storing more data than you have space for?

Yes.

All right. Tim?

Anthony, if I could just follow up. You talked about the move to kind of more consumption-based as a service. Is that unique to storage for you? Or are you looking across some of the other silos of infrastructure? And if anything that's different when you look at storage compared to the others? And then maybe for both. Could you talk about kind of when you look at your whole storage environment, is it all NetApp? Do you have others -- other competitors of NetApp? You don't have to name who they are, but maybe if you can just talk a little bit about how you choose a certain vendor for a certain application or use case, that would be great.

So to answer your first question, yes, we are looking to move to a consumption model across all of the infrastructure. We've already started that process with the compute platforms, and it's worked out well. Now we're starting to go down that path with the storage platforms. One of the challenges, of course, we face, anytime you change from a CapEx to an OpEx conversation is what's the cost construct, and we have to make sure that it makes sense financially in order to do that. Now the price points are becoming very competitive. So because of that, we can start to exercise in that manner. To answer your second question, so we grow by acquisition. So we may get one of everything depending upon what the acquisition is. But at the end of the day, NetApp is our standard. And so we move from whatever that alternative storage device may be, we move to NetApp over time as we integrate.

I assume you have a lot of stuff in your environment.

Well, we really looked very long and hard at our requirements that we had. Like I said, there's a lot of varied requirements from a control system to a 30-year archive. And the needs of the storage device really change in terms of subsecond response to, okay, how do I store this big chunk petabytes of data for long term. So in that case, once we started putting everything down on the list, we said, okay, how is it going to be available? How is it going to be reliable? How is it going to be manageable? And more importantly, well, how do you back it up? If something happens to the data, like ransomware issue, god forbid, how do you deal with that? And so once we looked at all of our requirements and we analyze them, we went through a pretty long path, a pretty long decision path, and we chose NetApp. And then once we did, it was very easy for us to go, okay, let's keep implementing the technologies that make sense for us. And they built on top of each other to give us a very comprehensive method for being able to handle our data.

Question in the back.

I'm Frederick Gooding with William Blair. Just curious. So we talked about AI earlier in this session. So just wondering, a, are you guys looking to more implement AI capabilities like within your storage environments? And b, do you see that as like a whole another budget in your guys' mind for like IT infrastructure? Or is that a part of like your existing budget just for storage in general?

So we are looking to implement AI where it makes sense for us to do it. Obviously, we have the concerns that everybody else has. AI has tremendous capability, but you also have to weigh the security concerns as well as ensuring that you have the ability to separate your environment in the event that you're using a capacity-based solution or you're operating in a shared environment such as Office 365 or Microsoft has implemented AI in their solution. So one of the first questions I asked is, okay, how are you making sure that if somebody's tenant becomes compromised, you're protecting [ game ]? And so those things you have to really focus on and ensure that you've got that validation. So yes, we do want to take advantage of it. We are putting it into our own products. But it's also the understanding and awareness of you don't want to let the genie out the bottle until you know what the bottle can do. You can get good things, but good things can turn bad very quickly if you don't have the right security, you don't have the right ethical controls and you don't have the right governance model around it.

We live in this time where you always are wondering if you're getting the right news, the right information. There's big buzz around fake news. And AI is certainly victim to that, right? The data that comes in, how you train your models and the decisions it's going to make out of that is then really key. You saw with the SolarWinds exploit, for example, that was a way of poisoning the well, and it impacted a lot of companies. And so I see AI as going to have a lot more positive benefits than negative ones, but there are things that governance is going to have to be key in terms of how we address those things, making sure that we have a very tight handle on how we're feeding that AI tool and then the decisions that are being made. And is there a check for the process that's making the ultimate call. If you just automatically go with it and there's no countercheck, that may be problematic. Livermore Labs, we've been experimenting with AI for quite some time. In our particular environment, I think we're still at the machine learning phase, again, because of where we are in our phase right now, trying to train the models and making sure that things are right. And as I say, some models are useful. So it's an iterative process until you get to the point of something that is going to be interesting in our environment.

Well, I think that iterative modeling part of AI is really interesting because I think so many people just want to think, well, you train the model and done, right? But I would imagine you're having to constantly fine-tune your pretrained models with new information as it comes in. How do you think about keeping things current and moving them along?

I'll give you an example. I mean, there are 2 areas in our program that we spend a lot of time in trying to get very deep, and one is optics inspection because we're looking for defects in our optics. As you put fluence through glass, you can get these little inclusions. And after a while, we have this optical loop process that recycles the optics. So it's always a question of, well, when do you land the plane? Or in the case of if, when you bring it down for maintenance to pull those optics out and refurbish them. That means less time for doing experiments, less time for science. So we want to make sure that we are very smart for when we do that type of event. The other issue that we spend time looking on is the small little BBs that we use for filling with hydrogen for actually being our main target. And we're pushing the edge of manufacturing as we know it. So they're always the pits and little inclusions that we need to do to make sure we machine what's possibly the most smooth surface that we know on the planet. And those things, we're using machine learning to be able to find out, is that a defect or is that a piece of dust or something that we need to deal with. And so those algorithms that we have to actually do detection is where we're spending a lot of time right now. Eventually, those things now are saying, okay, is a defect, is not a defect, are going to be smarter as our scientists get more involved with it to be able to make sure that it is making more right decisions than wrong decisions.

Is it a hotdog? Is it not a hotdog?

Right.

All right. Meta in the back.

Maybe a question for Anthony. It sounds like you guys have a pretty standard playbook for kind of bringing the company in and maybe starting with kind of moving to Keystone and figuring things out from there. I guess just wondering how has that playbook changed over time in terms of -- I'm sure the economics of just the scale that you've gotten has kind of changed some of the weighting. So just wondering, has it been more towards subscription? Has it been to kind of optimize on-premise? Just how has that playbook kind of changed over time?

So initially, we didn't have the technology available to really consider off-prem. And so we invested heavily in on-prem solutions. As the ability came along for us to get things in the cloud, NetApp brought those capabilities along, we started to move to those models much more expeditiously for a couple of reasons. Number one, enabled us to spin up that capacity much more quickly. But the other thing was we were able to get what we needed versus having to oversubscribe. And that was extremely beneficial to us. Now as we have the on-demand capacities available from solutions like Keystone, it gives us even greater flexibility because we grow by acquisition. We may close a location, we may close data centers, we may do all of these things. I cannot be reliant upon having something in a site that may go away at some point in time. So having moving to -- and moving everything, not just our storage, but we're moving all components of our infrastructure where it makes sense into the cloud. So we've moved to an SD-WAN solution that gets us off-prem. We're not reliant upon those things. So many other components of our environment are in the cloud when it's financially and functionality is feasible and worthwhile. But if it doesn't fall into those categories, then we remain with its on-prem and with a limited amount of footprint because we know the likelihood of those locations being impacted is very low.

All right. I saw some more hands. Sydney?

Sydney Ho with Deutsche Bank. I want to follow up with the questions on the AI question earlier. One of the speakers earlier talked about when you build these AI infrastructure, you buy the GPU first and then the next thing you buy is storage. Just from your experience, it sounds like there is a lag between them when you're talking about the iterations of evaluating the need. From your experience, what does that lead time kind of be like? And is it because the consumption model that you guys are going after, does that mitigate the need for buying storage in a certain period of time?

So there's pros and cons to everything. I would say that one of the biggest challenges we face is that not only do we want to make sure that the technology has the right capabilities, particularly from an AI perspective for me and my environment is a little bit different than yours. But for me, it's how do I have the ability to get automation, self-healing, all of those things that reduce any capabilities, any possibilities of having downtime, us having to have manual intervention to remediate anything that may occur. So moving to that on-demand solution, you don't have to worry about doing upgrades. We don't have to worry about maintaining it. We don't have to worry about that thing. But that's on our partner. So from that perspective, depending upon their adoption and integration of AI into their environment, so those things may come faster, they may come slower. Now if you're building it yourself and you're deploying it in your own environment like Phil is, that's a little different. In my world, I ought not to do that. I'm a pretty much a standard infrastructure and operations person that I got to be able to scale and get performance and reliability as cost effectively and efficiently as possible. In his world, he's doing a lot of very custom things. So his challenge is much more difficult than mine.

All right. Phil, so let's hear about that difficult challenge.

Well, if I understood your question, I mean, I think the -- it's a little cart before the horse because for me, it's all about the data first. You start collecting data on things that you find interesting that you've instrumented in your environment. And then you start having questions in terms of okay, what do I want to learn out of that? What deeper meaning is in there? And that's where you need more compute, you start doing things like data wrangling and figuring out how you're willing to make sense of this and you start organizing that data and have everything from data provenance and systems that you're going to be able to do to organize that and get deeper analytics from it. And then you find out, wow, I don't have enough compute to be able to answer that question. And you start limiting yourself in terms of, well, I can answer these types of questions but not these. I may not be able to forecast fully for where I want to be. And if you're lucky, you can get more hardware, more equipment and further on and answer those things all the way up until you go, man, I really could use a GPU. And so I don't see going to that path of wanting GPUs until you've begun the beginning of that. As a data scientist looking through everything saying, okay, I'm at that point now where I can't build those neural models until I have enough course to really chew through this. And it doesn't allow you to escape the hard work. The hard work is the data wrangling, the cleaning of the data, organizing of the data. All that stuff is time consuming.

Yes, David, down here in the front.

Sorry. So maybe for Phil. So I would assume, given your status as a national laboratory, nothing that you do is in a public cloud. It's all self-contained within the laboratory from an on-prem effective solution. So does that change your thought process? You mentioned at some point down the road, you need a GPU. Does that change your thought process in terms of what your ultimate infrastructure looks like down the road? Is it definitively you have to go down sort of an NVIDIA GPU type solution? Or is there other Ethernet-based solutions like Slingshot that are more than adequate to kind of meet the needs that you have? And so just curious about how you're thinking about that.

That's a very good question.

Let me think about that. So I'll say we have available to us GovCloud. So anything that is a FedRAMP cloud we can use where we have an ATO to be able to use one right now, and we have leveraged it for certain things. Certainly, we have outside vendors that we have as trusted partners that do things for creating diagnostics for us to recalibrating things and sometimes they send data back to us. And so we leverage AWS and S3 so that they can upload data into that, and we download from that, for example. So there is some footprint that the National Ignition Facility has on cloud. The greater lab, they have been using GovCloud quite a bit and pushing a lot of other things out to the institution, but I personally can't speak to that. We have, from the NIF, had some requirements, for example, where we did need some GPU capability. And there was a certain set of work that we could not farm over to supercomputing because they're too busy. The pattern is full sorry, and we said, okay, let's see if we can spin up some stuff on AWS, get it going and answer this question. And I think it's very powerful for being able to meet our gaps between the time to order, the time to deliver, the time to implement in our data center versus, okay, team, we've got some time to rent somewhere else and be able to do that. So those are powerful. But the other things that we look at in our environment since we have an on-prem building is some of the safety and failure modes and effects analysis. So we want to make sure -- I mean, there will always be an on-prem presence because you want safety interlock systems and those kinds of things to be locally controlled, and those are good things. So we are very careful about where we put things, how we compartmentalize things. I think some time back in the past, we consolidated until it hurt. And then you found out, well, we couldn't take certain parts of our infrastructure down for maintenance and patching and those kinds of things. So cloud fits into our strategy in a way of saying, well, how do we best leverage this in a way that is going to make sense? I think you and I were talking about some of this yesterday that some of the challenges are wanting to lift and shift a legacy application and just stick it out in the cloud doesn't make fiscal sense. You really have to collapse that whole thing and make a truly cloud-native application. So there are things that we're still working through. I wouldn't say we've got all the answers on it, but we are dabbling in it.

All right. Mehdi?

Just one quick follow-up clarification. You said the consumption model is helping you with OpEx versus CapEx and also helps with TCO. Can you clarify to me, are you talking about kind of apple-to-apple, like buying NetApp in the past, it was transactional; now, it's consumption? Or are you comparing NetApp consumption model to just renting a storage in the cloud?

No, I'm talking about comparing apples-to-apples. So if I was going to go and let's say I'm buying another on-prem flash storage solution from NetApp and I'm going to get that same type of solution in a consumption model from Keystone, what we have been looking at previously running the numbers is what's our cost per gigabyte. That cost, at one point in time, was not competitive. That cost now has become very competitive, and it has allowed us to have the flexibility to make this decision.

Okay. And how about the depreciation schedule? Are you now able to like assume a 10-year rise especially since it's OpEx, then you don't even have to...

We don't have to deal with that. But there's other benefits that we also gain, we get carbon credits and we don't have to worry about disposing of the asset at the end of its usable life, which we end up paying to do. That goes away. So we reduce our carbon footprint, which goes toward our getting to a 0 emissions model by 2035. So all of these things come into play as part of these decisions. They give you benefits by moving away from this CapEx investment into an OpEx.

Right. And just 2 quick follow-up. Does consumption model -- I'm sure it includes services or any kind of upgrade, right? And that makes a flash more durable compared to in the past. Like 10 years ago, we didn't know if the flash would be good. After 4 or 5 years, now it's -- there is a vendor that guarantees it in terms of the consumption model and services, right?

Yes. That's correct. And it takes the workload off of your staff from an operational perspective. You don't have to worry about upgrades. You don't have to worry about patching. You don't have to do any of those things. If there is any type of an issue, our partner takes care of it.

And then one last one. In terms of the back of COLD storage, how does the consumption model impacting the cost structure associated with COLD storage or for backup?

It really doesn't, unless you go to a different solution. For us, at the end of the day, our backup costs are our backup costs, whether we're backing up our storage that's on-prem or whether we're backing up our storage that's sitting somewhere else. It's still -- that cost is a set cost. So at the end of the day, it doesn't really impact us from a backup perspective. Now if we need to do something different to do that, obviously, then there's a cost impact. But we are able to utilize our standard products and not have to have a deviation, which is another one of the advantages of using a constant solution across a vendor because you have that consistency across the tools.

Thank you. Questions. All right. Down the front.

So Anthony, I know you mentioned earlier your business model -- or OpenText's business model is one of frequent acquisition. What have you learned in terms of how much of your infrastructure, over time, becomes more consumptive based versus what is stranded capital in terms of hey, we can't shut this down, it doesn't make any sense either from a flexibility or a cost perspective? Is there kind of a good general rule of thumb in terms of, for every dollar of IT spend, we try to move $0.75 of it to some sort of consumptive solution versus stranded capital that's more fixed on-prem or however you want to define it?

So I can't say that we've gotten to that level of granularity yet, but we -- I can say this is, as the technology has continued to become better, our goal has been to continue to move towards this consumption-based model because of the other things that I was just talking about. It reduces the workload on my operational staff. It reduces the need for us to have to be involved in maintenance and patching and upgrades, which takes a great deal of time. And in addition to the time factor, there's a cost factor associated to it because I don't have resources doing other work because they're focused on these activities. But over time, as the technology continues to improve and the price per gigabyte gets lower, it will continue to reinforce that value proposition as well as the carbon footprint benefits that we gain.

Scanning for questions in the back.

Jake Wilhelm with Wells Fargo. Could you talk a little bit about how you see the move towards continued disaggregation in the data center with technologies like CXL affecting your storage and memory architectures over the next several years?

I think a lot of that really depends upon the use cases. How do I say this? In the world that I operate in, we have a pretty well predefined set of parameters around the performance characteristics of our applications, our databases. Because we're primarily talking about HR and financial applications, those really back-end systems that really run the business, so those things are a little bit different than Phil's world, where he's the scientist that's doing all of these out-of-the-box things that are one-offs and they're trying to figure it out as they go along. My world is a lot more predefined. So I would say the impact to me is a lot different than maybe for other companies that have different use cases. I don't really have a lot of exotic applications and things like that, that we're supporting. So it's a little bit different for me.

Well, I'll say that we've tried really hard to make a lot of our environment predefined, which is why we've leveraged [ LexSpot ]. And we've got stove pipes in certain environments. So where things may seem disaggregated, what we have is pools of aggregation instead of one big aggregated pool. And as I mentioned before, that gave us some benefits for being able to take down different environments, being able to patch, being able to manage the unique workloads so that we're a little bit more fiscally responsible when it comes down to expanding our infrastructure to know, okay, this environment here needs more compute, this environment over here needs more RAM. Especially with our databases and some of the analytics, they're asking for a lot more RAM, as we're loading things more in memory and doing a lot more in memory compute. So those are -- like I said, we're trying very hard to not be this mad science, unique little beast out here. Again, we're using the same LEGO blocks that everybody else is, maybe a unique application for how we've assembled it, but...

We all have the same problems.

Yes.

Well, actually, I was going to -- well, Mehdi's got a question. Yes?

Sorry, just -- this is for Phil. Actually, nonstorage. Phil, as you look into the future, how do you see GPUs and ASICs path crossing? Do you see a day where there will be less of a GPU and more of an ASIC solution for AI application?

I hope there's both. Yes, I'm looking forward to a time when there's more code on chips so that everything from where the -- I live in a world where there's a lot of industrial Internet of Things. So I'd love to have the data as it comes out of those things to be able to be bagged and tagged and labeled at the central point. And as it's flown through my environment to be able to get a complete manifest of where it's been, what's accessed it because this is where -- as I mentioned, from an AI perspective, you need to know the provenance for where the data is flowing. You need to know if somebody poisoned that well for that information, if you're going to hope that this AI brain is going to make a good decision for you.

Is that a wish list? Is that actually a realistic target on the horizon?

Well, I do see that we're -- some things that are happening now and even some releases, I think that you guys are making is...

That might come later this week.

Yes. You might see some things that may be able to do localized or on-prem processing.

All right. Irvin?

Irvin with Evercore ISI. So Anthony, you talked about several of the advantages of spinning up storage in Keystone such as OpEx versus CapEx and the consumption -- the flexible consumption model. But is there a connectivity or low-latency advantage from being in a colo data center or peering that you get from peering that's worth mentioning as well?

So for us, we basically provide all of our storage where our compute is. So if we're using compute in the cloud, let's say, at GCP, our storage will be at GCP. If we're using an on-prem on one of our colos or in one of our data centers, it's going to be there. When you start trying to connect things from on-prem to the cloud is when you get into a major trouble with latency and application performance and cost because every time you take data out, dollars go up, right? So we don't do that. So it's really important that you ensure that you don't start going down those paths where, first of all, your applications are going to be severely impacted. But then every time you every time things get chatty, it's going to cost you a lot of money.

All right. Questions? All right. Well, we're getting close to the end of the show. So I'll ask kind of one question to wrap it up. It will probably take the full time. You guys are in really different industries. It sounds like you're both aiming for a more off-the-shelf approach to your technologies to make it a little bit easier for you. I was going to ask what the challenges you face are, but let's make it a bit of a positive. And what are the opportunities that you see ahead of you from your -- for your IT environments?

There are so many. The ability to deploy faster, to have greater reliability, to have self-healing, to minimize the need for having more support personnel and resources, which reduces our overall cost. They're exponential. The whole conversation about shift left, shift left, shift left. That's what everybody wants to do. So the better the technology is, it reduces the need for us to have level 1, level 2 people. The technology will take care of that. So that's Nirvana, right? So you can spin it up as you need to, you can deploy it where you need to, the time is minimal, all you have to worry about is getting the connectivity to where it needs to be and way you go.

What are you excited about as you look forward into your technology crystal ball?

Well, it starts off, like I said, with the data and being able to access more of that data. Our scientists are just ruthless when it comes down to accessing data and analyzing the data. And that's also one of the reasons why as you -- I was smiling as you're saying the cost of pull things back from the cloud. You don't want to do that, right?

You don't.

But so just being able to enable those discoveries, I think is a huge part of saying, we've done the right thing. Looking forward, as I mentioned, I'm enthused about AI, or I'd say, cautiously enthused about AI. There's more -- a lot more good that's going to come out of it than bad. I'm looking forward to its prospects as it relates to cybersecurity. It's almost every day you hear, some places getting hacked into. And I'd like to be able to take this on the offensive rather than a defensive with the 1,000 rocks coming at you. And even if you looked at it and you batted off 999 of them, that one comes through, but if AI can be that other layer as I'm going through layers of security, that something else is there saying, okay, we're seeing something else, that would be outstanding.

Are there any technologies on the horizon that you think are particularly exciting? Or just stuff getting cheaper and faster?

I'll start off. Cheaper and faster will help because the volumes of data are increasing. There is more fiducials that you want to grab in data sets, more aspects that you want to look at in order to be faster, better and more efficient going in the future. So those things are naturally going to increase data sizes. We talk about a lot in terms of how do we be -- how can we be more intelligent about what we store and not store everything. So as you do that, it hopefully will help the Moore's Law curve in terms of the demand for more space versus trying to be responsible when it comes to everything green and sustainability. So those things kind of come to mind.

Anything on your horizon that do you think?

I won't say specific technologies, but I will say, to echo Phil's point, cheaper, faster and more secure, those things are really important. The amount of data that we have to maintain and support is growing exponentially, and that doesn't go away. So to your point, how you manage it more efficiently, how you reduce the amount that you have to keep and only keep what you need but also being able to ensure that you can access it based on the use case and the manner that you need to from a performance perspective, those things are all critical. So those are the things that, for me, are really going to be important as we continue to go down this road.

All right. Right. Final call for questions. One in the back. No, it's great. It's better that it closes out with a question from you than from me.

Yes. No. So both of you guys kind of laughs about egress fees. And so I guess I was just kind of wondering from the perspective of, have you brought stuff back and paid those fees? Or is it just those end up being kind of stranded islands of stuff in the cloud?

So the answer is yes, which really brings to the forefront the criticality of doing the homework upfront. Everybody seems to think that everything in the cloud is free, which it is not. And you have to do the analysis to understand a few things. First of all, is the application a good candidate to go to the cloud? You have to do the homework and understand what the cost is to remediate the application because you don't want to do lift and shifts because what you're going to do, you're going to have a bill that you will not be able to afford and then you're either going to have to fix it when it's in the cloud or you're going to have to bring it back. And so you don't want to do that. But you got to do the homework upfront, understand the cost to remediate the application, understand the cost on your application teams because you're going to pull those folks from doing other work to remediate these applications. Are you going to pay a third party to come in and consult and do that work? Then you're going to pay the cost associated with the application running in the cloud if it's optimized. So you've got all 3 of those numbers, you've got to compare and compare that to what your on-prem costs are. Then you can make a fair analysis and assumption of exactly is this a worthy application to move to the cloud or not, because if you don't do that, you're asking for trouble.

That resonates with me quite a bit. The only thing I've got to add to that is -- and it's kind of obvious, right? Most -- in most cases, where your data sets are is where you want to do the compute. The problem comes in to where you find out that you've got some data on-prem or some data that's out in the cloud or some data split across multiple clouds. Then what are you going to do when you need to do that deeper analysis to pull everything in and actually manipulate that data. And so at that point in time, now you have no choice but to pay those egress fees as you're pulling from all of those and slamming it into your machine learning algorithm.

All right. Well, Phil, Anthony, thank you guys so much for spending some time with us. I really appreciate it. I appreciate you being NetApp customers, and thanks for talking to everyone.

Well, thank you.

Thank you.

Thank you so much. We really appreciate it. All right. You are free to enjoy INSIGHT. I know you're in high demand, so thank you. All right. Well, that concludes our program today at 3:00, the keynotes go up. So hopefully, we'll see you all there. And then again, for those who are here physically, cocktail reception at 5:00 where you can mix a mingle with all the speakers that you saw here on stage today and more as well as the entire INSIGHT attendee program. So thank you very much. Have a great day. There are boxed lunches outside.

Yes. They are killing it as always. I love Paul and the factor in that whole crew they really get it together. Look, we're really looking forward to watching these guys walk out over the next few days, and there's a lot more to look forward to here at the MGM Grand because NetApp hasn't just built -- let me just do this right here -- NetApp hasn't just built a conference here. You know what they built people, they built an entire data festival. Yes, that's right, a data festival coming on through. That's fine. You can do all of that all you want. It's no big deal. This is a super cool space where you're going to be able to connect with everyone in your NetApp Community if you head over to the Insight all Access expo area, you'll be able to drive the latest tech, pick up new skills and knowledge that will hone your it factor like never before. We've also got great workshops, hands-on lab certifications and special areas for our analysts, investor and public relations friends, great evening events and so much more. I hope you all get the chance to experience all of it over the course of this week. But let's remember, the NetApp Insight community extends far beyond the MGM grand. So please join me and saying hello to your colleagues watching the Insight live stream from home. Welcome to the party friends. Everybody say, hey what's up live stream? Yes. So glad you could join us, even if you're in your pajamas. Thank you for adding your unique it factor to the mix from all over the world. Meanwhile, here at the headliner stage, we are just a few minutes away from our Vision 360 session, the first of our keynote presentations. And who better to set the tone, than NetApp's CEO, George Kurian; and NetApp's President, Cesar Cernuda. That's right. They're going to be coming up soon with some very special guests. And one more thing you can look forward to is actually right here in the ballroom having a friendly bean bag toss with your colleagues, if you head on over to the game zone before the show for some cool festival activities, they got a giant Jenga over there. I'm challenging you to Jenga, by the way. And of course, it got corn hole, better start thinking about who you want to team up with and win some of those competitions over there. Speaking of teams, it's time to hear from some more members of the unstoppable NetApp A team. Please give a hand, to John Woodall from GDT and Trey Davis from Ahead. Look at that. You guys got fan club.

We've got a fan club.

Okay. John, where are you from?

Personally San Jose.

San Jose, man, are you enjoying insight so far?

I love insight. This is the -- so I've been doing it since 2004.

Wow. Do the math on that.

No, I don't want to that.

You're a veteran man. You're a veteran. And it's right. What about you, where are you from?

From Atlanta.

Is this your first...

No. This is probably my tenth.

Wow, so you're a veteran too -- all right. So what do you think is going to take your organization's it factor to the next level this year? We're talking a lot about the impact. What are you going to do with your organization to get there?

My organization, we're going to deliver these NetApp services and products that we're talking about this week and deliver it with excellence.

Any particular one you're super excited about.

A lot of cloud-related services, AI is a big one. So...

Awesome. John, what about you?

Okay. So what I'm looking forward to hearing from this show. So there are some people say that unified storage as a certain thing. What I'm looking forward to is hearing what NetApp's version of unified storage means, cloud on-prem all protocols, common control plane and in APIs...

I like this. I like putting them on the spot. I know what's going on. Give me the good stuff. Let's find out. That's cool. All right. So do you want to shout out any colleagues over to -- at your group. Anybody who want to give a shout out to your squad?

I got to call out my boss, Dan Mosley from GDT. He's in here somewhere I hope.

GDT makes some noise if you're in the building.

Yes. Somebody.

And Trey, what are you looking forward to the most here at Insight?

This -- I've already actually experienced it. Is this the energy of being here with the A team and friends and colleagues, you haven't seen in a while. It's not bad.

Well, you all have a lot in store for the show. They got a lot back for you. You've been seeing a lot of stuff behind the scenes. I can't wait for you all to see what's going to go down. Give a round of applause to Trey, give a round of applause to John. Thank you both for being here. The A team is in the building. Yes, they are. So -- with that being said, one more thing that I want to make sure that we go ahead and hit here is the fact that, you know what, first and foremost, can I give -- John, I'm going to give you the bean bag buddy. All right, hold on to that for me -- thank you. I appreciate it. You guys go over there and win some stuff, all right? Speaking of which, we just got a few minutes before we kick off this event. So make sure you're coming in, grab your seats, everybody grab your seats and get seated because we are about -- just about to be there a few minutes away from kickoff. And what do you say we get the blood pumping with another jam for my favorite crew... The factor. [Presentation]

Vision 360 session with George and Cesar on deck. You're not going to want to miss this, and that's going to close things out for this afternoon right here at the headliner stage. But that's definitely not the end of what we all have going on and our evening together. Because at 5:00 p.m. tonight, we're going to get it started at the official NetApp Insight Welcome reception. You are not going to want to miss this. We'll have more details on that later in the show. But rest assured, we're going to turn up like the NetApp community, only knows how to indefinitely won't want to miss that. All right, Vegas, we're just seconds away from getting the start of this show. Everybody you're watching on the live stream, welcome, good to see you were about to crake this thing up. So take your seats, get comfy, put those mobile devices on do not disturb, please? Thank you. Appreciate that. Now without further ado, it's time to kick the festival off in style in Las Vegas Live. It's NetApp Insight 2023, and it starts right now. [Presentation]

Las Vegas, welcome to NetApp Insight 2023, are you pumped to be here make some noise. Are you ready to show us that you got the impact or make some noise. Now, everybody from the left, in the middle and to the right. On the count of 3, I really want to hear you rock this build into Las Vegas right now, you haven't been together in 4 years. This is your moment. So on the count of 3, I really want to hear you all say NetApp Insight. 1, 2, 3, Yes, that's what I'm talking about. That's how you kick off a data festival people, let me ask you a question. When was the last time you've ever been to a data festival. There hasn't been one until now. So you haven't paid. Can we give a round of applause to The Factor of the House band, please, they've been killing it. Incredible. And again, let's give a shout out to our live stream audience who's watching us from all over the world. Welcome. Good to see you all. Thank you for being here. And I'm super excited to welcome you to the MGM Grand, which will be our home this week. Now my name is Mario Armstrong. I'm an MBC today's show technology correspondent, host of a podcast and a bunch of other good things. And I'm honored to be your host for Insight 2023. It is a pleasure to be here with you all. So I'm curious, as I've been preparing for this and getting everything together, I'm like, I want to know how many people are first timers. How many of you show hands our first timer like me? Wow, that's awesome. Okay, good. I don't feel alone. That's great. That's great. That's great. Okay. Now how many of you have been to Insight before? Wow. Yes, you guys, this is great, but there's almost a mixed crowd here, almost 50-50, maybe a little 60-40 there. Well, I heard it's the first time any of you have been back in Vegas for Insight since 2019. So don't call it a comeback, but you're here people, again, after 4 years, give yourselves a round of applause for getting here, flying here and making it happen. It's a big deal that you're here. Proximity is really important. Networking is hugely important. And whether you're an Insight veteran or a first timer like me, I'm super grateful that we have an opportunity to do this together in person. Now look, we have an incredible 3 days in store, and there's no stronger way to start than what you're about to see. Up next is our Vision 360 session, starting with the man himself, NetApp's CEO, George Kurian; and George is going to set you up with an insight primer covering the state of the industry, expert, AI insights from some very special guests and how to build your intelligent data infrastructure with NetApp. After that, NetApp President Cesar Cernuda, will take the stage and catch up with some leading customers and share their inspiring success stories. You're here to up your it factor people this year right now. So your journey is about to kick off. So without further ado, please welcome to the headliner stage, NetApp, CEO, George Kurian.

Thank you, Mario, and welcome to Insight 2023. It's super nice to have you all back in person after a few years. For those who are here for the first time, a warm welcome. And for those who have been here before, welcome back. Insight enables the once-in-a-year opportunity to bring together you, our customers, our partners, practitioners and thought leaders, NetApp technology teams and wonderful guest speakers to learn from each other how we can turn every disruption into opportunities for all. So let's begin today right here right now. As you well know, we operate in a world of growing risks where the rate of change and the impact of disruption is accelerating. These risks include geopolitical risks, macroeconomic risks, business and technology risk and changing customer demand. And if you are an IT as many of you are, you have your own set of challenges as well. You could include application and infrastructure modernization, getting rid of growing technical debt, dealing with an ever broader range of cyber threats, especially large state actors going after your digital assets and your data. You're dealing with unprecedented talent shortages where you are stressed to keep pace with the rate of technological advancement and you need to simplify, standardize and automate your environments. A few years ago, many of you thought that cloud was the panacea and was the answer that could solve everything and what many of our customers realize is that the pace of migrations to cloud, it takes time. It's complex and you've got a lot of learnings over the last few years. So you realize that cloud smart is probably a better answer and that you will operate therefore, in a hybrid multi-cloud architecture for a long time. Data and data management is key to success in this era. And we will show how data-driven businesses are AI ready and accelerating their leadership. Research that the Boston Consulting Group and Google conducted demonstrates the performance leadership and competitive advantage of data-driven businesses. They can unify, manage, protect and harness their data for business impact. They can understand a disruption and respond much more quickly than data laggards. So let's take a benchmark. Let's look at an important metric, like the percentage of companies that are able to grow revenues at greater than 10% a year. If you look at it, 30% of data leaders are expected to increase revenue by more than 10% by the end of 2024 compared to 13% of laggards. That's a huge gap. Now if you compare what's happened over the last few years, this gap is even wider than in 2022 were 16% of leaders and 11% of laggards expected the same increase. It shows that the benefits to being data-driven are large and expanding. And that is true, not just for revenue growth, but for every important business metric, cost management and productivity, customer satisfaction and retention, return on capital deployed and so on. Data leaders are advantaged substantially over data laggards, and AI is going to widen that gap even further. And because AI is the archetypal hybrid cloud workload where you want to experiment quickly on this cloud and scale your production environment potentially in your data centers or on the cloud. AI will also magnify the gap between the organizations that are hybrid cloud ready than those that are not. So let's talk about what it takes to be data-driven and AI ready. It's hard to be AI-ready and data-driven. The challenge is that data investments must deliver near-term value and at the same time, lay the groundwork for rapidly developing future users, while data technologies, the scale and volume of data, the diversity of data types are evolving at a rapid pace. And as we have seen, and I'm sure you, our customers have seen neither a monolithic top-down approach nor a grassroots bottoms-up approach have worked. There have been clients that decided, "Hey, we're going to put all of our data in a gigantic data repository". You remember the big data use cases of Hadoop. Those have gloriously failed as data types have evolved and analytic environments have progressed well beyond Hadoop. You think about the bottoms-up experiments in many of our customers. They create a plethora of pilots, none of which have gone to production. So what are the requirements for being data-driven and AI ready? It starts with having a cohesive data strategy and organization, knowing what data you will need to drive business impact and getting all the roles within your team, your data team, analysts, engineers, architects scientists to work together to design and deploy production use cases. Now let's talk about operating model and technology strategy next. In addition to having a cohesive data strategy and organization, operationally, you will need to keep data as a product. And what do I mean by that? That means that data for each domain, such as customer or product or supplier or vendor or employee is consolidated and treated as a single source that serves multiple applications and use cases. Today, when I talk to our customers, you focus on operating business processes or their underlying systems. Everybody focuses on the CRM system or the BI system or their supply chain system when, in fact, what you need to be ready to be data-driven and AI ready, is to put together a cohesive data set for a customer or an employee that's abstracted from the underlying system and forms the foundation of your data model that serves a broad range of applications that can transform your business in line with rapidly changing customer needs. So then let's talk about what happens to the next part of your architecture. A modern data architecture is the third part of what you need to be data-driven and AI ready, a modern data architecture enables you to rapidly respond to business disruption by balancing the need for flexibility, rapid change and transformation in some parts of your data architecture with consolidation, standardization, evolution and integration in other parts. We're not one to tell you transform every layer of your stack. That's silly because it maximizes the risk for incremental improvements and flexibility. What we say is transform the right parts of your data architecture and evolve and integrate and consolidate and simplify other parts of the architecture so that you have the right balance of risk, agility and efficiency. Let's discuss this more. Where your business process meets your technology architecture, you'll have a broad range of applications and use cases for data that need to rapidly evolve to meet the changing customer demands and the needs for your business. This involves not only using data from within your transactional systems in a unified manner, but potentially external data sources to create an immersive experience. For example, a customer portal that integrates multiple channels to create a truly immersive customer experience for your customer. You need flexibility at this layer of the architecture. But this layer of the architecture needs to run on a stable data model where data for each domain is consolidated and treated as a single source that serves multiple use cases above it. Operationally, as we talked about, data for each domain must be treated as a product independent of the underlying transactional systems. Let's now talk about the transactional systems. These data products sit on top of a flexible transactional data layer, where there is ongoing replatforming and modernization of applications and databases, data lakes, data fabrics, data misses to deal with a rapidly evolving nature of data, data types, the enormously vastly growing unstructured data landscapes and you, our customers and the scalability and feature requirements of modern AI analytics, open source technologies, you need to preserve flexibility in this part where the rate of change is technology is very high. And these need to be built on top of an intelligent, integrated silo-free data infrastructure that can evolve to meet the needs of all your applications and workloads, all of your data types in all the places you may need your data in the hybrid multi-cloud era. A modern data architecture is the foundation -- technological foundation stone of a data-driven enterprise that's AI-ready. To summarize, being data-driven requires a cohesive data strategy and organization and operational model to treat data as a product and a modern data architecture built on an intelligent data infrastructure. So let's talk about an intelligent data infrastructure because we know a thing or 2 about that. And that's what we've been working on. We are confident that NetApp can help every business make their data infrastructure intelligent so that you can turn data into a force to transform disruption into opportunity. Several years ago, we told you that the world would be hybrid and multi-cloud, and we have delivered the data fabric to enable your business to manage your data across any environment with performance and simplicity, whether your data is in your data center in a managed service environment or on the public cloud or a combination of all 3. Over the last few years, as we have worked with you, our customers and our technology partners, we've realized that customer needs for data management have expanded with more requirements for privacy, protection and governance, the need to support new more dynamic workloads like AI and cloud native and open source applications, the vast and rapid growth of unstructured data types that form the foundation of 80% of the data in most enterprises by 2028, and the need for observability, optimization and automation of an increasingly distributed dynamic infrastructure and intelligent data infrastructure builds on and expands upon the data fabric. It starts with silo-free infrastructure, then harnesses observability and AI-powered data services to enable the best data management actions. All of our capabilities are hybrid, multi-cloud by design, what a step forward for NetApp from the time we laid out our vision of a hybrid cloud data management architecture. An intelligent data infrastructure combines unified data storage across any environment with the world's best storage OS for all data, native cloud integration and unified control augmented by integrated data services for cyber resilience and policy-based data governance, supported by intelligent and efficient cloud ops solutions for observability, optimization and automation. To uniquely enable you to operate with seamless flexibility, get the most value out of your stored data and to tap into the power of AI to help you maximize productivity across your infrastructure and your teams. And we're excited to show you just how updates to our portfolio will make your data infrastructure intelligent. AI is the major disruptor of the modern IT era. Eventually, it will raise annual global gross domestic product by 7%. That is an astronomically large number, far more than what the Internet could have generated. The rewards, however, can be unevenly distributed because the leaders who are data-driven and AI-ready can dramatically outperform the laggards. We here plain and simple to make you part of the winners. We have years of experience in the AI market, working with industry leaders like NVIDIA to enable predictive AI and ML solutions for many hundreds of customers, some of whom you will hear from today. Trust us AI runs on data, data runs on NetApp. Our integrated data services help you derive the most value from your data and make scaling AI across your enterprise much easier. Tomorrow, you'll hear more about how we have the industry's best data pipeline for AI but before we do that today...

Hey, everyone. I'm Jeff Baxter, and I'd like to welcome you to a special segment just for our digital audience. You'll be treated to the World Premiere of a new what's the future video starting that watch all about AI. But I want to dig into AI a little bit more with one of our own resident experts, our SVP of Engineering, Octavian Tanase, Octavian, thanks for joining me.

Happy to be here again.

So Octavian, when we talk -- we often talk about AI in 3 different ways in NetApp. Do you want to summarize those for me?

Absolutely. So first of all, we're looking to help our customers deploy AI with efficiency and automation. Number two, we're looking to embed AI and ML in our products. And number three, probably what I'm mostly excited about is use of generative AI in engineering to improve productivity.

So since you're most excited about, let's start with that one, all right? How do we use generative AI within our engineering organization.

So generative AI, as the name suggests, it's about helping generate an outcome, sometimes code, maybe documentation, maybe translation of, let's say, legacy automation into something cool, something modern that our engineers could use and become more productive. So it's all about using perhaps open AI or open source LLM's that we deploy within our infrastructure to make sure that we maintain copyright and we do that in a very safe way to generate code, to generate -- to do assistant development through copilots.

Awesome. So NetApp is already taking advantage of AI to make our employees more productive. Now how are we embedding AI in our products to help our customers?

So this is an area where we have a little bit of pedigree. We've been doing that for quite a few years. So we started with open AI and predictive analytics. The intent here is to replace a lot of the heuristics that we build in our products over the years to make good decisions on behalf of the customers within the AI ML module. So think about tiering rather than being done based on some static heuristics, you have an AI module that learns from the data set and the I/O into a controller and does the right thing of understanding when data is called and could be moved to a more cost-effective storage there.

Awesome. So just a few examples there of how we use AI to make ourselves more productive, how we use AI to make our products even better. And you'll be hearing a lot more throughout Insight in all the keynotes about how we help you implement AI at your companies. But now I teased it before. I want to go ahead and introduce to you the world premiere of the [indiscernible] what's the future video, all about AI. Go ahead, Matt, take it away.

Hi there, I'm Matt [indiscernible]. Welcome to another addition of what's the future, which we're filming here from our U.K. office in Windsor. This episode on me about the AI monster. And I'm going to do 3 things. We're going to talk about how we came to the IT months as we built in the past. We're going to talk to Joe Baguley at VMware about how did we get here? And then we're going to talk about how do you think about your entire IT estate in the context of what that means when we look at AI. There's a similar pattern that happens every time something new comes along, and that is that we tend to create Monsters, So let's talk a little bit more about what that means. So there are typically 4 things that happen. First 1 is that some new innovation comes along and all of the businesses rush towards seeing how they can exploit this and get advantage from it. Step number 2 is that it's typically quite unstructured different groups go ahead and do things because they order benefit from this new innovation, which then leads to step 3, which is that every other group starts to catch up. So the benefits through this innovation are starting to become more minimalized. The problem with that is it's now become complex, which takes us to step 4 and step 4 is that we simplify things and then we go all the way back to the beginning, and we start the process over again. Sounds familiar, well we've done this 3 times before. If you go back to the late '80s and '90s, we used to have these monolithic systems, and those were gradually replaced over time with more modular midrange technology. It was more flexible. It gave people the ability to innovate at a much, much greater speed, it gave much more freedom. But with that freedom it meant different groups went out and made their own choices. The exchange team built the exchange infrastructure, the SQL team the SQL infrastructure, and on and on and on. We built first Monster. And then the second wave happened, VMware came along, and it reduced the complexity of these environments and increase the utilization and it normalize things for quite a number of years until the third wave, cloud, and we approach players in exactly the same way as we approach the first wave, lots of different groups starting to go out and choose the cloud they want to work with, the frameworks they wanted to use within those clouds, and that complexity has come back. And we're now trying to solve that complexity whilst understanding we're at the beginning of the fourth wave, AI.

Well, when we did the second wave, there was a very simple mandatory that we came up on a lot of our marketing, I suppose, which is abstract pool and automated. If you think that's what we did in the second wave to really change the world was through virtualization of network storage compute, we abstract pooled and automated that. Let's look at the third wave of multicloud. That's exactly what we're doing. Again, we're abstracting all those clouds. We're pooling them, automating it, simplifying it. So people now treat those clouds like they used to treat servers before within their data center getting that openness and choice.

So how do we stop AI from becoming the next Monster?

Guardrails. That's the #1 thing I'm talking to customers about right now when they're looking at the future. What we need to do is make sure that when people are going and using all these new technologies, just like before we had with cloud where there was shadow IT and everyone going off and using random bits of cloud [indiscernible] and building things on premises, and it was all the mess and then we had the same with Kubernetes. It's exactly the same we're going to have now, but people are off using random AIs all over the place. What they want to do is they want to make sure they don't end up in some kind of legal mess or logistical or operational mess around what they've built and then have to unpick that. So guardrails is really important. It's actually more about governance than it's about technology for customers right now.

So how important is AI to the future of many different industries?

It's fundamental in a way that people don't realize what we've got to now is a tipping point. And that tipping point is we're putting AI in the hands of normal people. And what I mean by that is when you finally get it out there to the mass market is where the magic happens, whether the nongeeks go and do things that we never expected or modeled. I know it's exactly what you're seeing now.

So this is the democratization of AI playing out in real time?

Completely, but it's also the democratization of technology. Back in the day when computers first started, a few 5 or 6 people knew how to program them. And then we got easier and easier programming languages we got more and more people could understand how to program a computer. Now you can talk to computers in natural language. We're having to understand how the computer works or what it does. That's absolutely transformational. That's really what we're seeing right now.

Goldman Sachs says that AI cost savings are going to increase GDP by up to 7%. So let's talk to an AI specialist at NetApp about what that means. Hey [indiscernible]

Yes, Matt, organizations are starting to see how AI can multiply value for them. Companies that are able to scale their AI projects are seeing 3x return of their investment. In fact, we're seeing customers who are getting almost 70% time savings from this -- from implementing AI in their business processes.

So what role will NetApp play in the future of AI?

If you look at the advancements of the AI technology, Matt, in the past 5 years, it was mainly because of 2 reasons: the availability of compute as well as the availability of the data for a lot of companies. If you think about it 10 years ago, when organizations are putting their cloud strategy. They didn't have AI strategy in mind. So they didn't think of bringing all this data together in 1 place and having access to the data wherever it sits, whether it's on the hyperscalers or on-premises is becoming a huge importance to advance AI technologies at different industries.

And what advice would you give to companies that are looking to take advantage of AI? Define your goals and metrics. Try to figure out what do you want to achieve with this technology, invest in talent and infrastructure, choose the right tools and platforms. And when you're choosing the right tools and the platforms, you've got to look at things like cost, performance, security, compatibility and then usability when selecting your tools and platforms. So basically, if you start with a set of goals and metrics, then you don't create that monster that we're seeing today...

So what's the future? Well, we need to learn from the past to make sure that we don't keep making the same mistakes again. We need to make sure we don't build another monster. And We're going to be able to do that if we start thinking about intelligent data infrastructures, finding consistent ways to do things in this world of AI, consistent ways of storing protecting, managing data of creating data pipelines of dealing with security. If we get that right, intelligent data infrastructure becomes the sword we'll use to kill the next monster.

Wow, that was absolutely amazing. What do you think Octavian?

AI, it's exciting. I really appreciate Matt's insights.

And if you want to check out more what's the future, go to [ net FTV ]. It's just about time to rejoin our Insight keynote, where we'll hear from our digital twins, George Kurian and Thomas Kurian. Take it away.

A lot of profound perspectives in the work and in the insight from Dr. Fei-Fei Lee. Let's now invite another guest up, someone who is working on optimizing data and infrastructure for large-scale analytics, data management and AI, all key considerations for technology leaders and business leaders, a person who needs no introduction, the CEO of Google Cloud and my brother, Thomas Kurian, Welcome, Thomas. Welcome to NetApp Insight. Thank you for coming. Thomas, we were at Google dot Next, where we introduced Google Cloud NetApp volumes, sort of incredible work that our 2 teams have collaborated on for many years and where we are seeing significant interest from customers. Today, one of the classes of applications I know which you are investing in significantly or fast scalable and easy-to-use AI offerings, including an AI platform, video and image analysis peach recognition, multi-language processing and so on. How are enterprises currently leveraging AI and what are the use cases that are delivering value for their businesses and why should business leaders bet on Google Cloud to help them embrace AI?

We've always looked at when we introduced Generative AI, we said the easiest way for people to think about it is to think of a digital persona for every role that can assist humans in performing the functions. And we see customers adopting the technology in 2 different domains. One domain is where they're transforming the core process of the company in assisting people and engaging customers. For example, Mercedes has changed the way that they're using AI models to help people identify if there are issues with the vehicle. Mayo Clinic is using our AI models to synthesize all the information out on the Internet as well as from the EHR clinical trial system. So when a doctor meets a patient, they have all that information in front of them searchable so they can find answers as they talk to the patient. Priceline is reimagining the way that you book travel. Rather than saying, "I want to find a hotel room" they say, why don't you have a digital travel adviser where you can say, "I'd like to travel to Las Vegas, watch 2 shows and I need to have -- I'm bringing my kids along, so I need a hotel room near the last show. So they're changing -- and there are hundreds of companies doing this. They're changing the way they interact with their customers. The same thing, we also see them transforming the internal processes in a company. Procurement people are looking at using AI to find contracts that don't have [indiscernible] warranties. Marketing people are using generative AI to create advertising, both images as well as audio and video, using models to do that. HR people are using it to automate the benefits and HR help desk. CIOs are using it to automate the way that their IT help desk works rather than have people have to reset passwords and things like that. Models are now doing many of these tasks. And finally, we have many companies who are using our AI tools to assist software engineers to write code, to do -- generate unit tests, to refactor code to document things. And so in every role in your working life, we're now seeing organizations adopt generative AI and models to assist people in doing work.

One of the questions that we get from clients as they consider about using their private data with AI tools is how do they deal with security and privacy concerns and things like copyright as AI becomes more mainstream? How do you all at Google Cloud, help organizations deal with those concerns.

We've always felt that what generative AI does is take AI out of the domain of a small number of people and make it available to every developer and every user in our organization. So we do that by putting together a foundational platform, we call Vertex. Vertex has all the tools that organizations need in order to use AI efficiently. First of all, it starts by protecting your data. We guarantee that your data is your data and nobody else's. It runs in a private virtual VPC. No one even Google employees don't have access to your data. So as a customer, you can build models securely and safely. We have a responsibility and safety framework that protects, for example, models from generating violent images, making sure models don't have abusive speech in their response. So we've all the controls -- we have 18 types of controls. We are grounding to make sure that model answers are grounded in fact and are fresh and current, not when the model finished training. You have tuning and distillation to shrink the model so that it can be the most efficient cost-wise and also latency-wise that it's very quick. And then we added search and conversations so that you can use Google search, but essentially for your data, and you can also build conversational applications, both to retrieve information and do tasks on your behalf. To expose enterprise data systems, we've exposed a technique called embedders and vectors. So you can take data and essentially open it up. And for all of those of you who have invested for so many years in storing your information in NetApp volumes, think about what we've done as putting Google Search in front of all the data you have, and opening up conversations so you can essentially chat with your enterprise data safely and securely.

So you mentioned Vertex AI, and you mentioned some of the awesome advancements you've made. How about we actually show our customers and partners the power of Vertex AI with NetApp Google Cloud volumes and to show you that, shall we do that? Let's bring up [indiscernible] VP of Engineering in NetApp's cloud engineering team. This is a live demo. There's no fake stuff here.

I'm excited to show you all the incredible things you can do today with Vertex AI and meta volumes. So let's pick up volume and get started. All right. Up in the corner here, I see my volume, I see the internal IP address that it has and that it's an NFS share. It has some folders on it. A bunch of files there, basically unstructured data. Now this is a demo about the generative AI capabilities of Vertex AI when mixed with NetApp volumes. So let's look at a use case that's simple, but real world. Let's imagine that we work at the marketing department of an enterprise company. We were just tasked with creating a product landing page that's supposed to be ready yesterday. Now we can have a look at all of the data that we have here and try and read through all them. But luckily, we have an AI that can sift through all of our data and find the relevant things. So let's ask Vertex AI about this topic here. Tell me about Google Cloud NetApp volumes. The product page is about NetApp volumes and almost instantly verdicts AI finds all the relevant files that you can see down here. It generated a summary based on the topic on our private data, not publicly available information and it even told us which pages it used to generate its responses. In addition to that, it's giving the key insights of each relevant file and with a click or a prompt, I can even generate some more, like, I want to see the summary of all the relevant files here or tell me which people are mentioned within these files. This is all possible by vectorizing our unstructured data that's sitting on NetApp volumes. And in this case, we have both the data and the Vector database indices on the volume itself, ensuring privacy for our data in the cloud. Now back to our goal here. We could go ahead and generate some more information with prompts or we could read all of these files and try to generate the product page, but we could also just chat with our data. So let's do that. Cool. Let's open up a chat here. I've created some suggestions just to get the conversation going. So I want to ask it first to Give me 2 line summary of Google Cloud NetApp volumes that I can use on a web page. Instantly replies, great. That looks good. Let's do some more. What are the key features of Google Cloud NetApp volumes. Yes, took a second key benefits. These are all typical things you would find on the product landing page. And the last one, what are the top 5 reasons to use Google Cloud NetApp volumes? Awesome. So we are getting all of these responses from the AI, but I want to pull it all together with some code. So let's ask Vertex AI, if it can create that page where -- Can you create an HTML product page with your responses? Cool. So it's going to be pretty boring if I don't have an image. So, let's use Vertex AI vision API to generate an image and I'll give it a prompt here. Let's give it prompt cloud -- let's say Cloud Data Storage artwork. Okay. The HTML is back. I can have a look at that. Yes, it's using all of the things that are asked for key benefits, top 5 reasons, great, and it's generating an image for me. That looks cool. I want to add that image somewhere in here. So I'll add it after the first headline there. So I'll ask -- Vertex AI can do that for me. Can you add the generated image below the first headline in the page? Okay. Now when working with code, you can both use the text model or the co chat model. It really just depends on the complexity of what you're working with. So once it generates that, I will see if it works, yes, look at that. It's added the image file right after the first headline. But this is live. So bear with me let's save this response to a file and then see what it looks like. Boom, -- isn't that just like Magic -- that's pretty amazing. -- what we've seen here is the power of Vertex AI with NetApp volumes, we've seen vector embedding and search, conversational large language models, image generation, code generation, all on our unstructured data right on top of NetApp volumes.

What about that QR code on the top right?

Right, right, right. Hey, proofs in the pudding, if you scan that QR code, you're going to see the page that we just generated was live.

Awesome. Thank you so much. It's really powerful to bring together the most advanced AI capabilities in the world with the best unstructured data management capabilities in the world to help you make breakthrough advances so easy and simple. I would ask all of you talk to your Google team, talk to your NetApp sales team and let's find a way to use the power of Vertex together with your unstructured data. One final question, Thomas. AI and particularly generative AI, consumes massive amounts of energy and requires higher compute power. How are you and the team at Google looking to strike the right balance between AI and maintaining your sustainability objectives.

Google has been carbon neutral since 2007. -- that 16 years now, we publicly said, and we are committed to being carbon free by 2030. And for us, it's not a question of AI or sustainability, it has to be we have to do both. There are many, many techniques that we've invented from data center design where data centers to run AI systems can be designed in a different way than data centers to run traditional computation. We introduced roughly 6 years ago, water-cooled AI systems because you get roughly a 30% to 40% lift in overall throughput and machine efficiency. We've invested in many techniques on compilers, for example, to really speed up both serving or inferencing and training. And more recently, we've also introduced techniques like distillation. So for example, if you're part of our preview, you can use a model that has many of the skills that our AI system has to write but you can actually run it on Gmail when you type help me write, and it runs directly on the Android iOS phone. So it's because we've shrunk the skills down -- all of that is designed to reduce the cost of power consumption, the amount of power consumption and also to improve the latency and efficiency. You saw how quick the model was in responding, smaller models are better in terms of latency and responsiveness as well. And so I would encourage all of you to try some of these capabilities. If you saw the demo, there was no coding or anything required. The models allow you to interact with them just using text and we're opening it up for everybody to do these things with our deep partnership between NetApp and Google Cloud.

Thank you for coming to NetApp Insight Thomas. I'm happy we got to share more about our partnership and looking forward to our continued joint success with Google Cloud NetApp volumes, you have an amazing solution for any general file use case, like VMware, SAP enterprise applications and databases and also as a platform to help you accelerate the use of advanced AI capabilities like Vertex AI with your existing private data. Now we've talked about AI, we now want to talk about security. We know that for AI-driven businesses, keeping your data secure is more important than ever. You saw from Dr. Lee's conversation that data is as fundamental to the success in using AI as the algorithms themselves. And in a world where there is a wide range of models, how you manage and use your private data and harness it for competitive advantage becomes often your only durable source of competitive advantage. We also know that for the last 15 years or so, cybersecurity has tried to protect everything; computers and desktops and servers and networks and identity and access controls and so on. And it's proving to be a really difficult challenge to keep the bad actors away from accessing your most important asset, your digital intellectual property, your software code, your movies, your unstructured data and so on as well as your important data like customer and employee data. One way to think about the problem is to stand it on its head. If you assume the inevitability of being compromised then you would prioritize protecting the most important asset from being attacked and accessed. And these assets are the crown jewels, your data and need to be over protected compared to other assets. And therefore, they require the commitment around investment and controls and reporting and governance to make sure that these your most important assets, your data and your digital intellectual property don't get compromised. Tomorrow, you'll hear about why NetApp is stepping up. To offer the industry's most secure data storage, we take our responsibility in ensuring the security of your environments and data estates and our offering to protect your assets with the strongest guarantee in the industry, backing up our position that we are making available to you, the industry's most secure data storage. Stay tuned for that set of announcements at our keynote tomorrow morning. But before that, let's hear from one of our nation's most respected experts about how security approaches and policies are changing to adapt to the new AI normal, Director, Jen Easterly, is the Director of the Cybersecurity in Infrastructure Security Agency, CISA. She was nominated by President Biden in April 2021 and unanimously confirmed by the Senate on July 12, 2021. As Director, Easterly leads CISA's efforts to understand, manage and reduce risk to the cyber and physical infrastructure that all Americans rely on every day. A 2-time recipient of the Bronze star and graduate of West Point, Director Easterly retired from the U.S. army after more than 20 years of service in intelligence and cyber operations. In her role now, she is relentlessly focused on innovating responsible secure AI for government, literally setting the standard for AI cybersecurity, please welcome Director Jen Easterly.

I thought they were going to do thunder struck.

Thank you so much for being here. A privilege to have you and your experience and thought leadership on this topic. Director Easterly, you've worked in the area of cyber security for the majority of your career from your time in the U.S. Army to the National Security Agency as a special assistant to President Obama, Senior Director for Counterterrorism before joining CISA. And then in 2021, you -- prior to that, you were the head of firm resilience at Morgan Stanley, what, if anything, feels different about this moment in security, particularly through the lens of the recent acceleration of AI capabilities and global conflicts and misinformation.

Yes. Well, first of all, great to be here. Thank you for having me. So I guess it's a 3-part answer. So I do think it feels different materially. And I think that is because of the incredible developments that we've seen over the past year, specifically with generative AI. I think it's captured the imagination, frankly, of people around the world. And I think it's the job of great leaders to be able to leverage the power of imagination, but avoid the failure of imagination. So you think about some of the excitement with these capabilities, but there's also a lot of threats that are implicated. And when you think about the global threat landscape with adversary, rogue nations, cyber criminals, terrorists what's happening in the Middle East, the acceleration of misinformation and disinformation particularly with the election coming up. It really makes you be thoughtful about how we develop these capabilities and how we govern them. And that's why it's so important to be able to put in place measures for responsible innovation, incredibly important because I think these are not going to only be the most powerful capabilities of our time. I think they're also going to be the most powerful weapons of our time. And if you think about the most powerful weapons of the last century, they were actually built and safeguarded by governments who are disincentivized to use them. These capabilities are being built largely by the private sector who are essentially driven and fiduciarily responsible to maximize profits for shareholders. So we have to come together between the private sector and the public sector to ensure that we can reap the benefits of these incredible technologies while mitigating the risk. So that's really the context. The second is how to think about AI. And very simply, I think about it as software. And I look at it through the lens of the short history of information technology, let's just go back 40 years to 1983 when the TCP IP protocol was implemented to allow computers to talk to each other. Well, since that period of time from the Internet to software, to social media, none of that was ever, ever created with security in mind. It was all about speed to market and cost and features and security ultimately was bolted on. And at the end of the day, that's why we have a multibillion-dollar cybersecurity industry. And I really think we don't need more security products. We need more secure products. That is really incredibly important. And that's what's behind our efforts around secure by design technology, technology safety, technology that is created, designed, tested, deployed so that security is the top priority. And I think about that for AI. It's just another form of software that has to be secure from the beginning. So third part, what are we doing? Well, I don't know if you've noticed this, but everybody in government is doing something with AI. We're all trying to catch up. And actually, the administration is putting out a very comprehensive executive order at the end, I think, in the next couple of weeks. At the same time, we are going to put out a road map that essentially lays out our operational lines of effort, and they're all about the nexus between AI, cyber defense and critical infrastructure. So first, how can we optimize these capabilities for cyber defense. There's a lot of amazing things we can do. That's really an extension of what we've been doing for years from machine learning. Second, how can we protect critical infrastructure from adversarial AI, not just cyber, but think about bio weapons, think about chemical weapons. And then third, how to assure AI systems. Again, an extension of some of the red teaming work that we're doing to ensure that we can identify, detect and remediate vulnerabilities in software. And to that point, I'm actually heading to London next week for the AI Safety Summit. And we've been working with our partners in the U.K., the National Cybersecurity agency to develop a secure code of practice for AI, getting terrific feedback from industry. We're excited to get your feedback as well and from our international partners, and that will be the first sort of thing that comes out in terms of what are the guidelines that developers need to adhere to as they're developing AI capabilities.

You mentioned the need to be secure by design -- and 1 of the kind of hallmarks of that is the responsibility of the technology providers and the solution providers, a large number of whom are here in the room to guarantee or at least step up in their commitment to make the products and solutions that we offer secure by design. Let's pull on that thread a little bit more. Where do you think we need to be? You mentioned quite a bit about where we are today. What would you say in a 10-year period, the aspiration should be for the industry to work together solved.

So it's a great question. Look, we are catalyzing a secure by design revolution. And okay, we've been talking about these technologies. We've been talking about Secure Tech for a long time, but the incentives have all been misaligned, -- as I said, the incentives are about cost capability, speed to market, not about security. So that needs to change. I was reading one of your blogs on ransomware, some of the capabilities that you've developed to deal with ransomware and there was a stat in there that said by 2031, there's going to be a ransomware attack every 2 seconds. And then if you look at the consortium for information and software quality, they put the cost of poor software quality at $2.41 trillion just for 2022, just in the U.S. And if you look at the cost from global cyber crime, it's upwards almost $10 trillion in the coming 2 years. So think about that 10 years, that's not sustainable. We can't live in that world, particularly because everything that powers our lives is digitized. Everything that we rely upon in critical infrastructure, our water, transportation, our communication, our health care, education is underpinned by technology base. So we cannot accept that this technology comes off the line with dozens and hundreds of vulnerabilities because we depend on it. And that's what we're trying to catalyze is this secure by design revolution. And we published the first document in April, and it was -- it laid out high-level principles for a technology that's secured by design. And we didn't put them in nerd speak. I mean I like nerd speak, but we put them in business principles because at the end of the day, this is about business. It's technology software manufacturers need to own the security outcomes for their customers. You know this very well. Two, software manufacturers need to embrace radical transparency and accountability. For what is in your software. And three, businesses need to organize for security. It needs to be at the very top or it's not going to be a priority. And it's really about boards and C-suite leaders and leaders of all levels. CEOs embracing cyber risk as a matter of corporate cyber responsibility, as a business matter. It's a matter of good governance. So we laid out those principles. We asked for feedback from industry, our international partners, from security researchers, from academia. We got fantastic feedback and then we launched the next version last week when I was in Singapore. with 13 countries, and it goes much deeper. I know your company is like 40% or maybe more of engineers in this whole room is probably technologists and engineers. I would love it if people go to our website cisa.gov look at the secure by design and give us feedback because really what we're trying to identify is what does right look like, both for software manufacturers, but really importantly, for consumers. Consumers need to understand what to ask for so that they are as safe as possible. And so we're really, really excited about this, and we feel like we're making some progress. But this is going to take a long time. I kind of joke about technology now is a version of unsafe at any speed. You remember back in Ralph Nader in the mid-60s, he wrote the famous both unsafe at any speed because car crashes were blamed on bad drivers. Now it took 2 years to get seatbelt legislation. We can't wait that long because our whole life is dependent upon technology. And just as we wouldn't get in the car without seatbelts, we don't want to be running around with tech that's inherently unsafe. So I would join -- I would ask everyone to join us and definitely give us feedback on the document.

Thank you. We certainly will. One final question. As you know, CISA and NetApp are working together through the IT sector coordinating council to develop best practices for AI security. We're also both partners of the joint cyber defense collaborative, the JCDC with other cyber Defenders. Can you say a bit more about these projects for those in the audience who may not be aware and as well as others, your offices leading that are charting the course for security and data management. There's probably many organizations here that could benefit and strengthen the collaborative.

Yes. So CISA is hopefully most people in this out against CISA, but we're the newest agency in the federal government. We were built Five years ago, our birthday is coming up in November to be America's Cyber Defense Agency. And so our mission is reduced risk to critical infrastructure, but we're not a regulator. We don't collect Intel. We're not law enforcement. We're not military. So everything that we do is buy with and through partners based on our technical expertise and the services that we provide. And so partnership is really in our DNA. And that's what's behind the information technology, sector coordinating council with Kristen Verderame sits on it, terrific partner, and then the Joint Cyber Defense collaborative, which we stood up based on new authorities from Congress that we got at the beginning of 2021 to essentially be one platform where you brought together the federal cyber team, CISA, NSA, FBI, cyber com to work with industry, critical infrastructure to understand what the threat environment is to put those pieces of the puzzle together and then to drive down risk to the nation. We started out about 10 of the biggest tech companies and now we're over 250, and we are very successfully leveraged to deal with some really serious threats during the Russian Invasion. And since then, we've been working very closely with the Ukrainians Log4J, all of the serious vulnerabilities and threats. And so it's been -- it's been a journey, and it is a journey, not a destination, but it really is a different way of thinking about partnership. It's not just plain old Hackney tired public private partnership. It's true operational collaboration that where you realize that a threat to one is a threat to many, where you have reciprocal obligations of transparency and responsiveness and the government adding value, where industry doesn't have to worry about sanction if they share information and where you have a frictionless experience. So you have scalable platforms for sharing information. And that's been really encouraging to see companies like yours and others joining not because they're trying to sell to the government, but because they realize it's the right thing to do for the nation because the capabilities where technology is around the world, it can really have an impact on driving down those vulnerabilities. So we're excited about that. And the last thing that I'll mention that we're doing with partners is our first ever cybersecurity public service awareness campaign. I think it's playing on some of the TVs around here is inspired by one of my favorite directors, Wes Anderson. And it really is, even as we do secure by design, corporate cyber responsibility, operational collaboration through the JCDC it's the imperative for all of us to be good digital citizens to make cyber hygiene as common as brushing our teeth or washing our hands. And so the basic things that we all need to do to keep us safe, our family safe or community safe, our businesses safe because as you well know, we can't do it alone, you can't do it alone. It has to be a partnership.

Absolutely. Thank you, Director, Easterly. Thank you for joining us at Insight. -- awesome and collaborative work and innovation and thought leadership. Ladies and gentlemen, Director, Jen Easterly. Thank you so much for being here. As we discussed earlier this year, the foundation of a modern data architecture is an intelligent data infrastructure. And so let's get to the third topic of today's discussion. An intelligent data infrastructure is built on the foundation of silo-free unified data storage. You can't try to do all of what we mentioned to replatform your transactional layer to build data as a product to operate in an integrated hybrid multi-cloud model to integrate security as a foundational tenet of your data architecture on silos, fragmentation, technical debt, risk from having diverse operating models and on and on. Our competition tells you that you need a silo for every workload. Let's take a step back and take a different view. As I told you earlier, Hadoop was trying to be a silo that promised to radically improve analytics and make the world of big data so much better. And there were a lot of customers that deployed Hadoop top to bottom as a top-down monolithic architecture. And as they have tried to realize as they realized that the world of analytics has gone way beyond Hadoop. The nature of data has changed radically from the original assumptions that they made when they created their Hadoop architecture. The advent of modern scalable computing infrastructures, event-driven architectures, Kubernetes as a scheduling platform for modern workloads, you got trapped, you got trapped in an analytics platform that is out of date, in a computing model that was out of date and in a data storage model that was out of date. That's what happens when you build a silo, you're trapped. We believe the opposite 1 architecture for shared storage, not only in your data center, but in all the leading public clouds with integrated data services, AI-powered observability so that you can operate your infrastructure with flexibility, with unification and integration so that you can evolve your transactional data layer very, very quickly giving you lower risk, greater operational efficiency and most importantly, taking new places that your business needs to go and where your data needs to reside in a secure private controlled manner. Our enterprise -- our enterprise-grade data storage portfolio is the industry's best, the only truly unified data storage for any app, any data, anywhere with unmatched integrated data security savings and a priority on sustainability and energy efficiency, powered by the best data storage OS for all data, native cloud integration and unified control. No other vendor comes remotely close to being able to deliver on these capabilities. And the power of having a single architecture translates into innovation capability release from us and to simplification, unification and the ability for you to mitigate risk while maximizing flexibility for you. You can take on any new application and any workload anywhere at any time you want, you can take the data from your on-premises environments to the public cloud. You can move from virtual machines to containers, you can build data security in once and manage it across your estate so that all of your assets, everywhere in the world is protected by design, and you can consume it the way you want, when you want, where you want. We are proud to announce some awesome new additions to our portfolio that expand on these promises tomorrow, come to our keynote. But before we do that, I want to show you our portfolio in action by sharing with you a conversation between our President, Cesar Cernuda and Mike Baylor, Chief Digital and AI Officer of Lockheed Martin. Let's roll the video.

Mike, it's great to talk with you. We're thrilled to share your story here at Insight. I understand you can be with us in person because of the incredible work that Lockheed Martin is doing. What you don't tell us about Lockheed marking mission.

Cesar, thanks so much for having me, and thanks for sharing our story. At Lockheed Martin, we solve complex challenges, advanced scientific discovery and deliver innovative solutions that help our customers keep people safe. as a global security and aerospace company, the majority of Lockheed Martin's business is within the U.S. Department of Defense and U.S. federal government agencies. The remaining portion of Lockheed Martin's business is comprised of international government and commercial sales of product services and platforms.

Mike, many of us think that Lockheed exclusively supports U.S. military and national security, but there's also impactful work you're doing in scientific research. We've heard a lot about the advancements using artificial intelligence in your center for innovation. I understand that you have developed cutting-edge technology using productive AI, using atmospheric and weather-related data to predict, reduce and even prevent that much for natural disasters. Can you tell us more about all these?

Sure. The center for innovation or we call it the Lighthouse is a unique integration, modeling, simulation and decision analysis in both real and synthetic environments. Within the Lighthouse, we build what we call the AI integrations lab. It's an internal ecosystem for developing and productizing AI solutions at scale for civilian and military applications. WildFire prevention and prediction is one really good example. Imagine if you could predict the fire where it might likely occur or detect active fires faster, Lockheed Martin's AI systems can use information on the current state of the fire and the local environment to predict its future behavior in minutes. This information can help deliver critical intelligence to assist firefighters in making faster, more accurate decisions. We're also working with digital twins related to this space. Digital twins can capture high-resolution, accurate and timely depictions of global conditions using current satellite and ground-based observations. We are developing a real-time digital twin recreation of a fire in assets to enable real-time AI-enabled, fully interactive mission management.

That's amazing. So lighthouse hardness is a massive scale about most very data, but it's able to use AI to read evaluate and predict in real time. What technology solutions make this possible, Mike?

Yes. We partner with NetApp and NVIDIA to make our work in the lighthouse possible. To take a step back, at Lockheed Martin, we use NetApp storage and cloud data services throughout our environment, not just for our research within the Lighthouse, but also to support our initiatives in other areas of the business, like Space and Aviation. NetApp solutions let us choose the right mix of resources to stay actual and innovate to accomplish our mission. In fact, the AI integrations lab runs largely on NetApp all-flash storage. We utilize Keystone, so we have flexibility to grow and innovate quickly without large CapEx. And as I mentioned, NVIDIA is a key part of that as well. The NVIDIA Omniverse Nucleus allows us to use AI and machine learning to ingest and make sense of all of this ongoing data collection together. This enables collaboration and data sharing across multiple tools and between researchers. And much of this is made possible by the data infrastructure that we've been able to build using NetApp and NVIDIA solutions.

Mike, thanks for your trust. We really appreciate it. What a powerful example of AI being used to prevent this faster and save lives. We are proud to partner with you and NVIDIA to make the solution possible, and we look forward to our continued partnership.

Thanks again, Cesar. We are excited to continue making great advancements in the realm of AI disaster prevention and beyond. Have a great time at Insight 2023.

To continue this discussion around the Lockheed Martin use case. I'm thrilled to welcome NetApp President Cesar Cernuda, to join me on stage Cesar welcome to NetApp Insight. Welcome, thanks so much for joining me. What an awesome conversation you had with Mike Baylor?

I did. And thank you so much for having me here, and thanks to everybody. It's my great pleasure to be here with all of you at Insight 2023 and actually to bring real customer stories here.

It's awesome. We love them. I know you've spent a lot more time with Mike than just the video shows, what have you been -- what have been your biggest takeaways or learnings from those discussions?

Yes. Actually, or you see I was quite impressed with the line saving impacts that they've been sharing with me. Globally, we've seen all the rapid acceleration of severe weather events, including wildfires. And these strategies have been catastrophic as everybody knows. And this is why Lockheed Martin efforts require the most advanced technology. And that's what they were sharing with me and you have seen AI as at the core of what they're doing.

Coming from California, it's so impactful to witness the life-saving use of technology. It's so relevant, the particular use case that Mike was talking about.

You're right. And actually, as you know, there is not just that were done in the U.S., but worldwide for organizations to fully take advantage of advanced AI and machine learning technologies, they require a future focused approach to data. As Mike shared the data infrastructure, at the foundation of the AI integration lab, is what really allows them to do this incredible solution that they share with us.

Awesome. So let's talk about that. At the core of the AI-driven solution that you just saw is that data infrastructure built primarily using NetApp or flash storage enabled by NetApp Keystone. Keystone has been the foundation as the project has grown and what started as a smaller internal AI use case has evolved to the current massive scale of the digital twin of the year. We're honored to partner with Lockheed Martin and NVIDIA to make this solution possible.

You're right. And actually, if you're interested or anybody is interested in learning more about the center of innovation. We sure to check out tomorrow. There's a session, I believe it's 11:15 a.m., I don't know the breakout room, but you can find that out. And look, it has been great to come here and to share this story with all of you.

Cesar it was a pleasure. And I know that you're welcoming another awesome customer year-to-date. That's why I let you take it away. Are you going to be back on states, right?

Yes. Thank you so much, George. So as we go on the transition here, let's get to switch gears to the next speaker and let's get into the world of fantasy because this new customer has been using the power of AI to bring the entire fantasy world to life. For the last 3 years, actually, as a leader in visual effects and animation you've seen the artistry and literally impacting movies like Avatar, Guardians of the Galaxy, Planet of the Apes, Lord of the Rings and many more. And here's a look of some of their incredible work. Let's play a video. [Presentation]

Please join me welcoming Kathy Gruzas, CIO of WetaFX. Kathy, welcome to Insight 2023. And thank you for joining me today to share about the world of WetaFX, I think everybody enjoyed the video. I recognize so many scenes from films that have taken the world by storm. And we're excited to have you here. I'm sure some of those are stories with us.

Cesar, I'm so happy to be here. Thank you for having me.

WetaFX has won countless awards for creativity, innovation and digital effects. The stories and experiences you've created for audiences go beyond what some of us can't even imagine. Can you share some examples with us?

We work on many major films in this industry. You mentioned Avatar earlier, which is a real standout for us. It has been such a privilege to work on this franchise as it's unlike anything that's ever been done in the entertainment world. The original film released in 2009 was the landmark 3D or stereo as it is on film of the modern era. The immersive nature of stereo meant the photoreal CG imagery needed to be at a higher fidelity than ever before to really draw the viewer into this magical new world of Pandora completely reshaping the way that we think about visual effects. And more recently, we took this to the next level in the sequel, Avatar, The way of water. This was the most ambitious visual effects film of all time. To give you an idea, WetaFX worked on 3,240 visual effects shots, 2/3 of which involved water so much artistry and research went -- artistry, research and development was involved. And we had to address many technical challenge along the way and really scale out our render and storage infrastructure. But I think the end result speaks for itself.

It certainly does. And what you have accomplished Kurian, the avatar film is unbelievable. I think the resolution is outstanding and i.e., margins image resolutions go up. The volume of data is multiplied by many times. So what technology does it take to pull all these off?

We developed a new simulation framework that allowed us to create detailed water at nearly every scale to fully capture the look, the feel, the movement and the reaction of water in -- against the CG environments and the characters. We also utilize new machine learning technologies to enhance the tools we use for facial animation as well as the blending of CG and live action sets and characters. This new tooling enabled the artist to create the amazing work that you see on screen. With in the background, bringing those pixels to life requires significant quantities of data generated by our complex pipeline and the management of that takes some wrangling as we say in the film industry, perhaps off to my amazing team.

Well, listen, hats off to your amazing team. That's for sure. And Kathy, you're steering WetaFX, total IT infrastructure and as well as balancing the requirements for cutting-edge equipment and technologies. Why has NetApp been the right partner for you? And I don't want to challenge you there. I'm super happy with that decision. But can you share a little bit more about that?

So NetApp is our storage of choice for our critical artist [indiscernible] as ONTAP offers the foundation and future set for this data to be stored, accessed and protected. We utilize your high-performance network attached storage systems on-prem and AWS FSX for NetApp ONTAP for both cloud rendering and virtual artist work stations. Our custom storage ecosystem we built to handle the punishing render compute workloads allows us to really push the performance envelope for large-scale film digital effects production and the rendering required for the high resolution, higher frame rates and 3D in ways that have never been done before. Movies like Avatar, break the molds. They elevate movie magic to new heights because of these artist enabling technologies.

Look, I know the abatements are just one of example of the visual effect magic that you bring to life. And I can wait to see what's next Kathy. I need to me that is fascinating to see behind the curtain as we're thrilled to partner with WetaFX and be part of that magic. And we're all excited to see your coming films projects. I think the Marvels, Better Man, King of the Planet of the Apes.

Thank you, Cesar. NetApp is one of WetaFX's longer-standing partners and you play a really important role in helping us continue to innovate. So thank you, and thank you -- thank you for having me.

Thank you so much for coming. Please applause for Kathy. Really impressive work. I think everybody sees the image and the hard work there. And WetaFX is uniquely designed that infrastructure has allowed them to exceed what was previously thought possible in a high-end digital content creation. It's such a privilege to see and hear what our customers are doing to innovate every day. There's many cases out there. And I would love you to go and share as you are here inside with each other, some of the things that you're doing, so we all can learn from each other. And with that, let me welcome back George Kurian on the stage, George?

Awesome story, WetaFX has truly redefined visual effects and that requires performance capabilities second to none. It's amazing to hear how much data goes into a modern film. Maybe you can share with our audience what the original Avatar was like and what's the new one is like.

I might even ask a couple of questions about that. But as you heard from Kathy, they're able to really push performance with made-up technology and the foundation, right? And for example, Kurian, the first avatar movie. You know how many petabytes a needed back then one petabyte, one, which was super big at that time on precedent of data for a single field. What do you think about the latest one, the way of water. You know how many petabytes? I know you do.

Amazing numbers...

23 petabytes at the peak, 23 petabytes, and they rely on NetApp storage as the art is facing for tier for both films to provide performance and resilience. And that's what I really appreciate about the partnership with them.

That's amazing Cesar. Thank you so much for joining me sharing this groundbreaking work.

Thank you, George. It's my pleasure to be here with everybody. I'm excited for Insight 2023. I'm excited to see you all. I'm sure I'm going to see you in the breakouts and as well in the opening, I'll be back on the stage on Wednesday with some very special guests. -- let's enjoy Insight -- thank you, George. Thanks, everybody.

Thank you, Cesar. It's going to be a fantastic insight. Whatever the disruption you face, you can be better prepared by being data-driven and AI-ready using an intelligent data infrastructure. We can help you with that. Today, however, we are also cognizant that to build a more equitable and just future where the access to the capabilities that the modern world offers, we need to bring together the next generation of data leaders. And so we are today announcing an expansion to our commitment to doing just so. They will play an important part in shaping our future. And so we are expanding our investment in our data explorers program, offering our curriculum free to individuals, partners and customers and up to $100,000 in micro grants to communities in need to ensure that we're all equipped for tomorrow. I get to witness the impact of the work that this program does with little kids who get to dream big dreams kids like me who had somebody give them a chance. And I'm super proud of this opportunity. I ask that you all join us in making data explorers have a profound impact by bringing together the next generation of data leaders. Let me close today by sharing that we're in the business of helping you, our customers become and remain data leaders who can convert the seemingly constant stream of disruptions into opportunities to do that, to become data leaders that are data-driven and AI ready, you need to have a cohesive data strategy and operational model to treat data as a product and a modern data architecture built on the foundation of a silo-free intelligent data infrastructure. We delivered the data fabric to enable -- seamless hybrid cloud data storage, mobility and protection. And today, we shared with you our vision of the road map ahead as we chart the next phase of our journey together to enable you to solve the needs of modern workloads, demanding AI applications, the rapid growth of unstructured data, the increased threat of cyber actors taking your malicious actors going after your most important asset to your data and intelligent data infrastructure expands and builds on the data fabric, combining unified data storage, integrated data services, infrastructure, observability, automation and optimization, for you to have an infrastructure for data that's disruption-proof and ready to translate disruption to opportunity. Building an intelligent data infrastructure is a big step to being data ready, data-driven and AI ready, which is what will set apart the winners from the [ also-rans ], -- and tomorrow, you will hear about real proof points of technology leadership with the world's best unified data storage portfolio having exciting advancements, the world's most secure data storage giving you the ability to use your data securely and privately anywhere you need it and the world's best data pipeline for AI, allowing you to combine the world's most advanced AI tools with the world's best data management. We have got an awesome program for you at Insight. We are honored to have you here after many years, and we are excited at the prospect of our community, You, our customers and our partners to learn from each other. Have a fantastic week, have a great insight, Godless. Be well. See you tomorrow.