Hello everyone and welcome to theCUBE's live coverage of VAST Forward 2026 here in Salt Lake. I'm your host, Rebecca Knight alongside Dave Vellante, my co-host and analyst. I would like to welcome Fresh From the Main Stage, Jeff Denworth. He is the co-founder of VAST. Welcome, Jeff.
Thank you. 10 years. Wow.
It's a decade. Nearly a dozen.
You're entering your tween years as a company.
True. Double digits. Well, that's actually, let's start there because this really feels like a moment of truth for VAST. I'm curious to hear you talk a little bit about what's different now at this company versus, I mean, obviously at your start 10 years ago, but even just a year ago, in terms of the life of VAST and also where we are in this moment of AI transformation.
I did this for... We just had our company kickoff yesterday. Our one-day company kickoff, which things are pretty compressed in terms of time at VAST. And there's a quote that Hemingway writes around, it's from The Sun Also Rises around going bankrupt. Somebody says to the main character like, 'How long does it take to go bankrupt?' And the answer is, well, very gradually and then suddenly. And I think about that with respect to VAST in the moment that we're in right now and the idea of exponential curves. When we started the company, we were super small, couldn't even see us on the map of the technology space, like a little spec. Then we grew and grew and grew and still wasn't very big. And then when you start growing, compound interest is the eighth wonder of the world. And we're now way bigger than we had expected to be even a year ago. And we're seeing that in all sorts of different dimensions. We have people that are coming to us, not to sound like too full of ourselves, but we declared sold out about four weeks ago and people were like hostile about the fact that they couldn't get in here, not a lot, but like enough where it was like, this is a little strange. We look at what's going on right now in the market with respect to like flash capacity and compute infrastructure. And what we've realized at the beginning of this year in particular was that we have such an outsized impact on the market that we're moving like significant parts of the industry right now. And these things come up on you all of a sudden where you don't actually realize it until you're in it. And then the interesting thing that we've been thinking about and trying to conceptualize is, well, if you're on an exponential curve, where does it go from here? And what does the next five to 10 years look like? And we have no clue, but we're ready for the ride.
But you're still not that big.
I mean, in the grand scheme of the industry, you're pretty tiny.
I mean, you're like a lever because you're in all the right spots, but I mean, I don't know, the last time... At one point last year you guys talked about a run rate number.
What can you tell us today? You probably can't say anything.
We have like a legal team now.
But what was the last public statement?
I think I'm allowed to say a few things. So we're at a run right now of over a billion bucks. Excuse me, half a billion of ARR. And so there's a ton of deferred revenue. One of the ways we sell is customers make large commitments to us and the deferred revenue is in the many billions of dollars. And so I look at it in terms of scale and perspective. You think like, okay, well, what were some of the biggest tech software IPOs in history? And we're of that level where we could come out and make like a very, very significant splash in the market with one big distinction, which is if you look at the core business fundamentals, most big tech IPOs are underpinned by this idea or question mark about sustainability, like how long can this run for? When are they going to become profitable? When are they going to stop burning cash? And we're already to the point where we haven't been burning cash for almost half a decade and we've been like accounting wise profitable this year, which means that we can just keep sustaining this for as long as we want to.
But in terms of the market share of the macro, I mean, I don't know. Your TAM, it's bigger than the storage TAM, you would agree?
Okay. It's like the AI TAM.
We look at it in two ways. One is we take a view of, well, what's the market that VAST is going after? And it's storage, it's database, it's compute infrastructure from a software perspective. And then, well, who are your competitors in that? And that's kind of like everybody.
But then the question is like, who are we disrupting? And AI becomes the agitator in the market that is the thing that invites us into the door to do our thing. And so that TAM, by my rough estimation, is about $200 billion. And I don't think we sell a drop of it if customers don't need to change how they're doing things, but because AI is such an agitator in a good way in terms of like enabling business productivity and gains and things like that, that our TAM is roughly like a $200 billion year TAM.
Right. There are competitors that are envious, but not because you're necessarily disrupting them directly. You're getting dragged into the AI.
That was one of the big realizations I made the other day. I looked at a few of our competitors who have had no market share gains where they've operated in the last five years, like one, 2% growth rates, which is like not even tracking with inflation. And we came out of nowhere and we're selling today billions of dollars a year in terms of software contracts. We had hardware that were dragging either in public cloud or private cloud. It's a big business. And those other vendors are still selling, which tells me that we're operating in a space that most other companies aren't. And in certain cases, we have like almost full market share, but yeah, it's like all the new stuff.
As a co-founder and somebody who's... Renen talked about principles, not rules. Is one of those principles that efficiency or do you ever think, wow, to really scale this thing, we need to scale our go to market. You clearly have product market fit, you have go to market fit. You could scale that and actually not be as profitable or even cashflow negative. Would you ever consider doing that?
I think the question is what underpins that principle and what is underneath that is a thrust towards independence. And so we never want to be in a position where we're dependent upon any other party to give us money to operate our business, right? And so the interesting thing is that the cash balance has to grow as the company grows because the safety net needs to be larger. And so they're directly correlated. And efficiency isn't the goal, independence is the goal. So we can do what we want to do. We can do what customers ask us to do and we're not subject to the whims of external forces, I'll say.
Well, so actually, let's talk about the customers because you had your sales kickoff yesterday. You are running on fumes, Jeff.
It's your first ever customer event today. I'm curious about the conversations that you're having with customers and where they are maybe further along than you expected and where they're perhaps more stuck than you'd like.
Oh, I think it's interesting to understand our customer dichotomy before we get into that. The company was born with an idea that we could power AI pipelines and there weren't any existence. So we basically just started helping people that did large scale computing, like high performance computing centers like traders and life scientists and things like that, with an idea that AI would come. And when it did come in 2022, what we realized is that that would first manifest in some of the world's largest AI cloud data centers where AI labs were those original customers. And so if you think about platforms like CoreWeave and Lambda and Crusoe and Nscale, these are all like built on VAST platforms and they are servicing the needs of the largest AI labs. And there's a bunch of them here. There's a bunch of clouds here, there's a bunch of cloud customers here, but then there's this whole other part of our business that's getting the enterprises ready for their kind of like large scale deployment of AI, AI agents in the market. And those are much earlier in the process of figuring things out. And so it's a whole spectrum. I wish I could say there's like one archetype for everything that there's not.
All right. So let's get into some of the details of vision and announcements. Let me start with, what is an operating system and what's the difference between an operating system that orchestrates things like memory and IO and an AI operating system?
Okay. So let's have the conversation about what an operating system is before we get to VAST.
An operating system is a few things. You have a run time that you can run your applications on. You have a file system that you keep kind of like your unstructured data in. Sometimes you have an index for that file system that's in the form of a database or something like that. You have a set of security policies and you have device drivers that basically make the hardware that's underneath it run. So we've been building for the past 10 years and last year was the moment where we got to the point where we realized that what we were trying to call this thing didn't marry up to what it actually was, right? And so started as a storage product called a universal storage. Then we said, okay, no, it's not that. It's an AI data platform and data platforms are pretty popular. People could kind of like, you know, you could understand those words. And then we started building geo distributed pipelines on the platform and we had these SDKs and you could just run AI models natively within it. And we took a step back and we said, 'Okay, you've got this global compute scheduler on top of this global file system with a global index and we build when we're allowed to all the way down to the device driver level so that we can get the maximum efficiency out of hardware.' And at that point we ran out of ideas. We didn't know what else to call it, so we just called it the AI operating system. Now there are other people that use the term, I mean, it's kind of like so generic that it's tough to figure out what it is. We have customers that call their cloud platforms AI operating system. You have NVIDIA calls some of their stuff...
I know they call their...
Dynamo was an AI operator. Does it work with training? No, it's more for inference, but it's just a kind of a general term and it does require some explanation, but anything that was like specific, like let's say Dynamo, people like, 'What's a dynamo?' Okay, well I still have to explain it anyway, so why don't we get through it.
So then VAST is like... So why us? And I think the thing that I talked about on stage is that I think you can define VAST by two different characteristics besides Renen's frugality, which creates efficiency in the company and things like that. Kidding. First, I think of us as being very first principles minded. And when we started the company, we spent a year just working on architecture and an engineer's inclination when they get a bunch of venture capital is just go start coding. And we resisted that in a way where we could just spend a year thinking. It was almost like a think tank, about seven guys in a room, probably pretty stinky. Some of them were here, hopefully not stinking up this room. And the idea was, well, let's envision the first 10 years of the product and let's envision it for all the forms that we wanted it to be. And it wasn't just a storage conversation. We were building asset data structures for distributed database transactions. We're building geo distribution tech capabilities. And then Rome is never built in a day, even if you have the architecture and the plans. So we started building and we built in a way where we can monetize the first aspects of the product. The second big characteristic... So the first one was first principle is like, let's just solve for the most fundamental problems in distributed computing so that we can just get this thing that pays dividends as we expand the capabilities. And that brings us to the second part, which is second characteristic of VAST that I think is really special is one of ambition, right? We're on a march not just to reinvent how people store and access their data, but basically to reinvent the entire data processing stack. And so that involves us thinking about and endeavoring upon basically computing with the entire data industry and the entire compute industry and the entire storage industry, all with a single software product that we think kind of defies conventional limits in a way where customers just get gravitated to the gains that they get, not only from each individual use case, but when everything comes together and you kind of see it magically working altogether. And so first principles thinking coupled with real ambition, like wanting to not be constrained by anything is how we define ourselves. And that gets infused into this AI operating system we built.
So before we get to the announcements, which I want to get into, but I have to ask you about culture because it is something that Renen talked about and you just talking about first principles and this ambition really gets at the culture of the company and this mission drivenness of you and Renen and the seven stinky guys and now the big company that you are today.
I'm going to get in trouble for that one.
Sorry, sorry. Okay. We'll... Anyway, how do you maintain that? Because I mean, it's one thing for two founders to get together and say, and bounce ideas off each other and have this immense sense of urgency and these high expectations for themselves and for each other. But then as you're building the company, it is harder to maintain.
And you might make things harder for yourselves, in fact, if you have those expectations.
Well, I don't know quite how to answer that question other than to say, number one, we don't actively have culture as a program. In fact, we have a very, very small HR team that basically helps us with recruiting, but it's not to impose a set of behaviors or rules on the team, right? And that is by design. We don't want to get bureaucratic. And then number two is we spend almost no time talking about culture, but we do try to operate according to those principles that Renen talked about and once a year we get together. Last year, it was his 42nd birthday. Nobody realized this until the end of this talk, but he gave us 42 principles for the company and the 42nd was about the secret of the universe, which is also 42. On his 42nd birthday, it was just like totally a meta thing. So anyway, so yeah, it's just...
It's really embedded. Yeah. And how you operate, but it's not something you talk about. Interesting.
Okay. All right. Well then let's get onto the announcements.
To me, the architecture is what is most fascinating because what you just described is you didn't set out to build a faster box. I wrote a piece years ago Thinking Outside The Box asked.
Yeah. And not a lot of storage companies think about acid properties when they're starting to...
Right? And so that to me says a lot. The whole stack is reforming. Thank you. NVIDIA's started it all. But when you think about networking, totally changing. When you think about the data stack has to totally change. But not a lot of companies other than VAST are really thinking about that, how storage has to change. And my question is...
I think there's a reason for that, by the way.
So explain that. But my real question is, how far can you go? Because there's this whole intelligence layer emerging, and it's unclear. Is it going to be the... Everybody's saying SaaS is dead, it's not, but maybe parts of it are. The LLM vendors clearly have a say there. You guys have ambition. There's this system of intelligence that you're feeding, how far do you go there? But why is it, to your point, that not a lot of people are thinking about that in the storage space, and how far do you go up that stack?
I'm going to try to not sound arrogant here, but I think it's in part because we've cornered some of the market. And as a result, there's not a lot of space to innovate by other companies that don't play or don't get to go to the party that we do. And so as I think about it, the fact that we are welcomed to work with some of the world's largest model builders, it puts the fuel in our tank to go and build to what they're looking for in ways that we don't see a lot of other companies even having these conversations, much less thinking about co-engineering with them. Like this year is a big year for us where we're building a customer engineering team, which is not a customer team, it's not an engineering team, it's not a suite. We have 15 different types of teams. Now we're building one just for AI builders and AI labs that we can co-invent technologies together to scale up intelligence next order or two of magnitude. And that's a very special place for us to be. I got a text message from one of the world's largest AI builders yesterday, and he just said it's go time for something that he's working on that's really special. And that just inspires us so much to work so much harder.
Jensen talks about extreme co-design. A lot of his extreme co-design is within NVIDIA, but of course it's with the partners as well, no question.
But you're talking about kind of a similar, the integrated stack... and the partners. So how far can you go? Do you see VAST as feeding that system of intelligence or do you actually start to get into being that system of intelligence?
We're never going to train a model.
