 Welcome back everyone here live in Palo Alto, California, and John Furrier, Dave Vellante in our studios for the VAS Presents Build Beyond. We are here with the CEO, Rene Halleck, co-founder and CEO, great to have you. Thank you. Congratulations on your success since 2019, VAS data, doing all the hard work, announcing the data platform. Really impressive, big idea. It is. This is actually an idea that we had a long time ago, but it was too early to reveal it to the world, and so we wanted to do it in phases. We had just had the keynote announced with all the experts on really kind of breaking down kind of the story. There's a lot going on here in the platform. What was the inspiration? What was the vision? I think the inspiration was always artificial intelligence. Even before we started building the storage system many years ago, it was all about how do we give fast access to a lot of information to enable these new algorithms and these new applications. And this is one more step that we make in that direction. So, everybody now saying, oh yeah, we saw this coming, and I kind of have, you have a little more credibility because what you've announced is you can't just invent that overnight. But yeah, two questions. One is, you launched the company formally in 2019, but I look at what you've built and all the functions and the services and I'm like there's no way they could build that in three or four years. So you obviously started before that. So if I go back to when you actually really started, you know, AI, it's been there forever, but you had to make a bet that it was gonna be like it is today. And you didn't know that it was gonna be the AI heard around the world and chat GPT, you didn't know that. So what did you know that gave you confidence to basically give up everything and start this journey? We didn't have confidence. We didn't know that it would work. We didn't know that this path was the right one, but we thought that if it was possible to do the things that we presented in this keynote, it was worth a big bet. And I remember we didn't start the company in 2019, we launched the first product then. We started working on this beginning of 2016. And if you remember at that time, the most AI could do was find cats on YouTube videos. It was a big leap to think we can build something that will enable what we have today, let alone what we see in the future. We did it because we thought it was important and we took inspiration from others that came before us. I remember seeing a video with a gentleman by the name of Danny Hillis. He built the original thinking machine. And he said, we wanted a name that we would never outgrow. And so we wanted a mission that we would never outgrow and we're on that mission. Okay, so thinking machines, learning systems. How is your vision of AI different than what we're seeing today? Which is probably a lot of pattern matching, maybe a lot of statistics. Can you explain for the audience sort of the difference in terms of how you see really the potential of AI versus what we're seeing and touching today? Yeah, today I think we're, well, first of all, a lot further along than I thought we would be in 2023. And definitely a lot further along than we were when we started, but it's still not real intelligence. You can tell that today's systems are parroting back information from the internet to us in a very natural form and allowing us to ask questions in a natural form, but they're not generating new ideas. They're not creating new things just yet. And we hope, we think that that's coming and that they can help us solve the hard problems. Yeah, I mean the interaction, obviously it's amazing, but you're right, you can basically game the system and tell it to tell you something and you can control it in a way. And in a way that's good, but it's not as much sort of independent machine thought as you would expect. And so it's interesting you say you're further along. When you say we're further along, you mean the industry is further along. Yes. And so you would expect that, do you expect that pace of change to continue to accelerate? I do, I do. As more people see that something is possible, they try it and they innovate on top of it and that acceleration happens just from that. And when you listen to Ilya from OpenAI talk about, it was like an epiphany. And they underestimated the importance of scale. And then once they figured out how to program the GPUs and they'd be able to ingest all that data, the scale and vision became groundbreaking. What do you see as those sort of groundbreaking milestones ahead that will enable these types of learning systems? So everything that we did from day one was centered around scale, around performance at scale, resilience at scale, ease of use at scale. Our entire architecture was meant to break that scale versus everything else trade off that has existed in storage and data systems forever. And that is the reason we did that. We saw that with more information, you turn from a quantitative advantage to a qualitative advantage and new abilities emerge as you give these systems more data. And so we built this for not just exabyte scale but beyond that. And today, four and a half years after we launched the company and have a lot of exabytes on the platform, we're seeing that that scale is not just theoretical, it actually works. You guys have the databases in there. You got the data store, front structure, database for structured. The data engine and the data space is interesting. And I think those are unique things that jump out at me. When did you kind of know you had it, had the right answer here? Because I think this is one of those things where you got the right answer for AI generation deep learning. But now the operationalizing is going to be there. So first of all, great customers, Pixar, large scale, but they're agile. They're actually making product improvements. Their applications is the movie and they're doing more. We heard that and we saw the science side of it. The producers on the creative side is increasing. So we're seeing now this next gen application with large scale data. When did you know you had this answer right? When did it kick in? I don't know that we have the answer right today, but we're definitely I think in the right direction. And what we do, our process is a very simple one. We talk to our customers and we ask them, what do you see, what do you need? They help us see around corners because they know where their challenges are. And what we try to do is listen and aggregate. And when we hear the same problem over and over and over again, we ask ourselves, how can we leverage our unique architecture to solve it in a better way than it was solved before? And this data space idea came from a lot of media and entertainment customers initially where they have teams all around the world and they have production and post production and editing and they need that collaboration across very, very large data sets and they had a challenge to solve. And when we figured out how to do it, we came with that to a lot of other industries and we realized everybody wants a single global namespace across cloud and edge and device. We have autonomous driving car companies. They want the cars to be part of that namespace. We have genome sequencing companies. They want the sequencer to be part of the namespace. In effect, it gives computers access to the natural world, direct access unfiltered. And I think that's a big piece in the puzzle of having them generate ideas. Multiple cars, part of that namespace because the Byzantine fault tolerance is a problem in fully autonomous vehicles, right? And so if you can have the cars talking to each other, you begin to attack that problem. That's right. And you're envisioning doing that obviously and it has to be real time. Yes, everything needs to be real time. There's no more batch processing. That's a big part of our value proposition. You don't need to compromise on time or on amount of data anymore when you have a platform like this. I love how you guys mentioned the data developer because Dave and I have been talking about it. The first time I've seen anyone use that term in their presentation, so awesome. What do you mean by that? Because we've been seeing this trend. We've been trying to, we just call it data developer because it's what jumps out at us where the developers who were writing code want to use data in their application and enable their applications on their own to work with data, which is kind of obvious on one hand but not obvious on the other. So where do you see this data developer coming from? Who is it? Can you just share your vision on the data developer? Yes, I think all developers will be data developers relatively soon. The paradigm is flipped on its head. We used to have applications and they were at the center and they were writing to storage systems and reading from storage systems and from databases. Now we have data and data flows in and data needs to be manipulated and cleaned and authenticated and encrypted and all of these functions on data as it flows into a system means that the whole development paradigm needs to change and be data centric and we're seeing that happen in these new workloads that we discussed. I think it'll happen across all the workloads. You know, it's interesting, Dave. We've been saying also on theCUBE that cloud is horizontally scalable. Your distributed namespace is a distributed system that's scalable. So you get horizontal scalability but vertical specialization in domains, you mentioned industries are all affected. They are their own linguistics, their own data, their own jargon, that's proprietary. So how do you see that horizontal scalability and vertical specialization? Cause that's where the AI lives and that derives but also they need all that other data. That causes a compliance challenge and all kinds of data management issues. What do you let free and how do you manage it? How do you build that in? Can you share your vision on how you guys achieve that horizontal scalability and that vertical specialism? Yeah, so I think you have two layers of horizontal. The first one is the hardware layer that's underneath us. We partner with companies like NVIDIA to leverage the latest and greatest of what they have to offer. And then you need that software infrastructure layer that makes it easy for the various verticals to focus on their applications and that's where we come in. We think that it's a very similar pipeline that you see across these verticals. We can't tell the difference between a genome and an image but from an underlying infrastructure perspective, you have the same tools and you need the same tools in order to make those developers successful. How do you see your ecosystem evolving? Obviously you've got, you mentioned technology partners like NVIDIA, we were down in HPE in April so you've got to go to Market Partner there and they're OBMing your file system, et cetera. How do you see your ecosystem evolving? As we progress, what we see more and more is clouds and not just the top three clouds that everybody knows and talks about, we see more and more specialized AI clouds and we see the big clouds spilling over sometimes into these more regional clouds in order to provide services to their customers faster and we are enabling those clouds. The software infrastructure layer that they are building their clouds on top. So for example, you could see a financial services industry or a company saying, I'm going to build my cloud, same thing in healthcare, same thing in the industry 4.0, name and industry, energy, they're all going to have their own clouds is essentially what you're saying. They're probably going to be running a lot of their stuff in the public cloud. They're probably going to be thinking about oil rigs. It is the edge, right? Space is another awesome edge. That's right. We have that systems in all of those places today on ships and cars and airplanes out in outer space. This lightweight instance allows us to span the globe. So you're an enabler for those clouds and you'll partner with other enablers of those clouds and those industry clouds will be consumers of your technology. That's correct. So where the customer store all the data? Where is it on drives? Is it in a data centers in the cloud? We heard multi-cloud is in there. Take me through the vision of how this gets deployed. Where is everything set? It depends on the customer. Some customers prefer to have everything very close to the chest. Other customers are more open and want the ability to run on top of multiple public clouds and collaborate with other organizations through that. We're agnostic. Wherever that data is, we will manage it and we will make sure that it is accessible to the applications that need it. I noticed you guys are very standard space. I heard open a lot in the messaging there. How important is that to you guys? Very. I think if we were to try to change the way everybody works, it won't work. We need to make it easy and simple and that is why we support standard interfaces. From the early days, we supported NFS and SMB and S3. Very, very standard on the outside. Very unique on the inside. Same will continue going forward. Right, and I got to ask you a question since you have such great customers and the video was awesome and impressive. Scale jumps out at me. And the benefits at scale, you see when you're there, you really, it's hard to predict what they could be. What do you see from your customers that are at that scale with data, that they're learning, that they're positioned for, what comes out of that value? What's the value that comes out of that? We saw Pixar was a great example. They could reuse and reuse the characters and then even if it gets bigger and bigger, they still could flex on the cluster. They enable them. What are some of the benefits you see that companies get at scale when they're managing data with your system? So it allows them to start to analyze natural information rather than just store it and have people able to access it or analyze numbers and rows and columns of a database like they did before. Now they have full access to everything and GPU access to everything which allows computers to generate insight from information that just a few years ago, it was unthinkable. And I think that scale, if you look at our system, it's three to four orders of magnitude bigger than any other system out there. And it's because of the type of data that we're storing and our customers are now generating insight from it. They're improving their business because they have access to more information. So don't hate me for asking this question but if you had 100 points to allocate toward luck and good, how much is luck and how much is good? Well, I think we work really hard to build our luck but we're definitely lucky to be in the space that we are at the time that we are. I'll say. And we're very fortunate. The AI is a great tailwind and it's a great time. People are looking to store in their data for many, many reasons, so congratulations. In the final 30 seconds we have left to explain to the audience who are curious what is then this data platform about? Build Beyond, we're talking about the data platform, not storage. That's right. It's a system, it's an operating system, it's a platform. What is the VAS data platform? So you see from our name VAS data that we've been thinking about this forever. Storage was the first piece of the puzzle. I think there's a triangle here around storing unstructured data, making sense of it and making it into a database where you can query and understand and then running functions on all of this information, which is the data engine and all of that across geographies will allow things that aren't possible today and that's what we're building. One more quick one because I think I got it wrong. You said the fastest growing in a keynote infrastructure company. That's correct. We've been growing since we started selling this faster than any other company that we know of in history, in this space and beyond that, we've been growing in a very efficient manner. We're not burning through cash in order to fuel that growth. We've been generating cash for the last few years and I think that too is a unique way of breaking that trade off. Well certainly people looking to manage their data is a great time to be in this business and the great platform. Thanks for coming on theCUBE. And great to see you and thanks for coming on and sharing your vision. Thank you. Okay, we'll be right back. Jeff Danworth coming up next co-founder of Vast on theCUBE here live in Palo Alto for the Vast Presents Build Beyond. I'm Jeff with Dave Vellante. We'll be right back.