 One thing I'd point out is, well, FlashBlade, one of our products is Scaleout. FlashArray, our first product, is not Scaleout. You know, Scaleout isn't a capability for a customer. It's an architecture in how you build the product. You know, when I scale out, I have more complicated software. I have more components. More components lead to more failures. Right, if I have a piece of memory and it's going to fail at a certain annual failure rate and I have 10 pieces of memory, I'm going to fail at 10 times that same rate. So Scaleout introduces complexity, it introduces more components, and then you have to say, what do you get from it? So if our customers needed a lot more performance than we're delivering, if they needed a lot more scale than we're delivering in the FlashArray product, we'd then react to that and go build Scaleout. Where the FlashArray sells, we don't see that as a major market need. It's more of a niche. Where FlashBlade sells, then there is much more of a need for that and that's why FlashBlade was Scaleout from day one. The TAM expansion really is following where solid state takes us. You know, we've gone from a world that was where, believe it or not, most computers still had mechanical systems operating them. It's sort of like having a mechanical calculator rather than an electronic calculator, right? We had mechanical disks in our computers, literally spinning rust, right? And it's only been in the last decade where a semiconductor, where solid state has taken the place of that, called Flash, right? Well, as that continues to get less expensive, we now can bring not only Flash performance into disk economics, but more importantly, now we can finally have modern software that is driving the need for having greater flexibility with our data. As data grows, it now, we say it has gravity. That is, it gets heavy. It gets hard to manage, hard to move between different environments. And now, a lot of infrastructure operators are spending much more time managing their data, managing the storage systems for their data than they are managing anything else in the data center environment. We want to eliminate all that. We want to automate all of that. You know, on the theme of decades, two decades ago, every application had its own individual communication stack. There were dozens of different protocols and a dozen different networks in every company. One decade ago, every application had its own custom hardware stack and custom operating system stack. Well, today, there's one network. It's called the Internet. Today, every application, every server is virtualized, allowing mobility, and yet storage is still static. We want this decade to be about making storage and data dynamic and really responsive to the needs of the application environment. Sure, so it's a deep relationship that's only getting deeper, and it's really at all levels. It starts with the executive alignment, and you think about Charlie, Giancarlo from Cisco. We've got a lot of just common cross-pollination there. But now it extends, certainly, to the field level. Tom and I are doing a lot of planning together in terms of having our teams go after common use cases. But now it extends to engineering as well. We had a UCS director plugin that we've had for some time now, but Pure is now first in terms of having integration into Cisco InterSight. So we are first and only to have storage integration into Cisco InterSight so that Cisco and Pure customers can really manage their environment from one console. So a lot of simplicity, the single SAS interface for managing everything. Tom, why Pure, why first with that? Well, Nathan, he articulated it well. You can look at the executive level. We talked about Charlie, but even all of our Cisco executives, but also to the engineering. We started really strong with the field sales teams. But even if you look at the little things that our customers noticed, but a lot of people may not like the internal development of validated design guides, use cases, we churn them out with Pure as our top ecosystem partner more than anybody. There's a lot of work being done. Our customers see that, and it's really helped drive our go-to-market together. It's really a very strong strategy. And what is the type of data that's going to be the best fit for it? There are a lot of common patterns for consumption in AI, speech recognition, image recognition, places where you have a lot of unstructured data, or it's unstructured to a computer. It's not unstructured to you. When you look at a picture, you see a lot of things in it that a computer can't see, right? Because you recognize what the patterns are. And the whole point about AI is it's going to help us get structure out of these unstructured data sets so the computer can recognize more things, the speech and emotions that we as humans just take for granted. It's about having computers being able to process and respond to that in a way that they're not really capable of doing today. Absolutely, absolutely. Yeah, no, I mean, I think it's been a really exciting conference for us so far, like you said, a lot of payload coming out. As far as the building of the bridge of the hybrid cloud, this has been, I would say a long time coming, right? We've been working down this path for a couple years. We started by bringing some of the cloud-like capabilities that customers really wanted and were able to achieve into the cloud back into the data center, right? So you saw us do this in terms of making our on-prem products easier to manage, easier to use, easier to automate. But working with customers over the last couple of years, we realized is that as the cloud hype kind of subsided and people were taking a more measured view of where the cloud fits into their strategies, what tools it brings, we realized that we could add value in the public cloud environment. The same types of enterprise capabilities, the same type of features, rich data services, feature sets, things like that, that we do on-premise in the cloud. And so what we're looking to achieve is actually quite simple, right? We want to give customers the choice whether customers want to run on-premise or in the cloud. That's just a choice of, we want to make an environmental choice. We don't want to put customers in a position where they have to make that choice and feel trapped in one location or another because of lack of features, lack of capabilities, or economics. And so the way that we do that is by building the same types of capabilities that we do on-prem in the cloud, giving customers the freedom and flexibility to be agile. Sure. Two-year-old startup, headquartered out of Bellevue, Washington. And we really focus on two primary businesses. We have a blockchain business and we have an AI business. In blockchain, we are one of the largest blockchain cryptocurrency hosting companies in North America. We've got facilities, four facilities in North Carolina, South Carolina, Georgia, and Kentucky. And really the business there is helping companies to be able to take advantage of blockchain and then position them for the future. And then on the AI side of our business, really we operate that in two ways. One is we can also co-locate and host people just like we do on the blockchain side. But primarily we're focused on creating a public cloud focused on GPU-centric computing and artificial intelligence. And we're there to help really usher in the new age of AI. How does Pure actually take that word simple from a marketing concept into reality for your customers? Yeah, I think simple is the most underappreciated but biggest differentiator that Pure has. I was recalling for someone, you talked to Cause earlier today. I had a conversation about three weeks into the existence of Pure, excuse me, with Cause. And we were just debating, I mean this is before we wrote any code at all about what would be Pure's long-term differentiator. And I was kind of like, I will be the Flash people or high performance or whatever he's like, no, no, no. We're going to be simple. We are going to deliver a culture that drives some plus into our products and that will be game changing. And I thought he was a little crazy at the time. But he's absolutely turned out to be right. And if you look over the years, that started with just an appliance experience, a tent card install, just a really easy environment. But that's manifested itself into every product we create. And it's really hard to reverse engineer that. You know, it's an engineering discipline thing that you have to build in the DNA of the company. So how do you see the partnership with Splunk just in terms of supporting that TAM expansion the next 10 years? So analytics, particularly log analytics have really taken off for us in the last year as we put more focus on it. We want to double down on our investments as we go through the end of this year and in the next year with focus on Splunk as well as other alliances. We think we are in a unique position because the roll out of smart store, right, customers are always on a different scale in terms of when they want to adopt a new architecture, right? It is a significant decision that they have to make. And so we believe between the combination of flash array for the hot tier and flash blade for the cold is a nice way for customers with classic Splunk architecture to modernize their platform, leverage the benefits of data reduction to drive down some of the cost, leverage the benefits of flash to increase the rate at which they can ask questions and get answers is a nice stepping stone. And when customers are ready, because flash blade is one of the few storage platforms in the market that is scale out, bandwidth optimized for both NFS and object, they can go through a rolling non-disruptive upgrade to smart store, have investment protection. And if they can't repurpose that flash array, they can use pure as a service to have the flash arrays the hot tier today and drop it back off to us when they're done with it tomorrow.