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Renen Hallak, founder and chief executive officer of VAST Data, joins theCUBE’s John Furrier and Dave Vellante at theCUBE + NYSE Wired: Robotics & AI Infrastructure Leaders 2025 event. The discussion focuses on architectural innovation in AI infrastructure and the enterprise demand for scalable, high-performance data platforms.
Hallak shares how VAST Data is evolving to meet the full AI lifecycle, from training to inference, while enabling real-time performance at scale. The conversation also explores partnerships with companies like NVIDIA and the g...Read more
exploreKeep Exploring
What is the current status and recent developments in the storage industry, particularly concerning Vast Data and its data operating system, over the past year?add
What changes are occurring in the field of AI and how are organizations adapting to these shifts?add
What changes in data analysis and infrastructure are necessary for handling unstructured data as compared to traditional big data techniques?add
What are the challenges and considerations for governance in the context of data security and compliance as AI systems evolve?add
What changes are occurring in the approach of big banks regarding AI applications and infrastructure?add
What are the key constraints and challenges related to scale and performance in modern data workloads compared to older systems?add
>> Hello, and welcome to theCUBE here in our Palo Alto Studios. I'm John Furrier, Dave Vellante. We are here for theCUBE's series
on robotics and AI leaders. This is part of our NYSC
Wired community presentation. A great event last night.
We've got a great guest here. Renen Hallak is the co-founder
and CEO of Vast Data. Renen, great to see you again. Thanks for coming on
this special AI leader series. Appreciate it. >> It's great to be with you.
- So infrastructure leads, it's our second year doing this and you've been on multiple
times, but the world's changed. So give us a quick update. You got CHIP software, geography of the big issues in the industry. Storage is now everywhere, but the data operating system, which you guys are doing is key to that. Talk about what's changed for you in the past, let's say 12 months.
Renen Hallak
>> Well, everything. The world shifted from training to inference. We're now moving from
inference to agentic workloads. The organizations that are doing AI are
changing from experts, the model builders, the big AI
clouds to enterprises, all of that means that the weight is
shifting from compute to data. And these organizations need
to be spoon-fed, need something that's simple, they need it to be secure. They need to basically be told how to adapt these new technologies. >> Two years ago you were on the stage, you launched the company's product. It's been worked on for a
while. We had that conversation. A lot's changed since then, but the original North
Star's kind of playing out. Architecturally you're
starting to see the enterprise, certainly the hyperscalers and large- scale systems providers
are all moving quickly, now the enterprise is heating up. Is the architectural
change happening the way you originally saw it? Has it changed a bit? Is the market changed at all
in terms of the orientation? A lot's happened on the chip side, but AI is certainly more
hyped up than ever before.
Renen Hallak
>> Yeah, I think more and more the different parts of the architecture are starting to shine. When we were talking large-scale training, you needed fast access to a lot of data, but that was basically it. You didn't need resilience.
It wasn't a critical workload. Latency didn't matter,
distributed systems didn't matter. Now as we make this
shift to inference, all of those things come into play, and especially as we
add agents into the mix, that's a whole new dimension. Before we had people and data,
now we have people, agents and data, and that introduces
all kinds of questions around what data can these agents see? What data can they communicate
to us, to other agents? How do we have observability
into what they're communicating between themselves? How do we have reproducibility
of how they gave that answer two years ago? All of these things are data related, and I think more than ever, we're seeing that data is also helping
accelerate the compute aspects. Things like KV caches are
moving the onus onto the data platform in order to save NGP resources. >> So I want to dig into this. I wrote a piece last month,
did a deep dive on VAS, called it Unpacking VAST Data's ambition to become the operating system
for the thinking machine. And what I wrote was
VAST is attempting one of the most audacious
pivots we've seen from an infrastructure specialist
in the last decade, talked about the $2 billion in software, et cetera, et cetera. The new AIOS layers distributed
scheduling, event streaming, vector search, database indexing, and an agent execution
framework on top of this. Disaggregated shared
everything you call it tays. >> Days. - Days, sorry. So
I wrote why this matters. We think the AI error is
going to be defined less by monolithic applications,
more by distributed agentic, kind of what you were
just saying, workflows that continuously embed, enrich and act on data might,
when I first heard this, again audacious, I said,
"Wow, operating system for AI. " I thought that was Jensen.
So okay, so help us understand where you fit in that whole value chain.
Renen Hallak
>> So we work very, very
closely with NVIDIA. In fact, all of our
latest announcements were combined with NVIDIA. If you saw at COMPUTEX, Jensen
gave us more than a minute of stage time in his keynotes. We are working very closely
with his enterprise team. We're working very closely
with the software teams. And so when you build that
operating system, I like that analogy because
it's right in the middle between the hardware and the application. And NVIDIA obviously built
all of the hardware pieces, the GPUs, the DPUs, the networking for these new AI workloads, and they're also in that
enterprise application layer. They're building NIMs
and NeMos and blueprints, and we are there as that middle of the sandwich piece in
order to enable all of this and bring it together. Why is that important? Again, as we move from a
world of experts to a world of laymen, we need that operating
system to make it simple, to make it widespread. I like to give the analogy
of the personal computer. In the '70s, you needed
a PhD in computer science to operate a computer. In the '80s and '90s everybody could do it because Windows made it
simple for us to do it. That's the stage that we're
at from an AI perspective, and that is the piece that the world, I believe needs in order
to make this next leap. Now, why is a data platform a good place to start when building
this operating system? Every one of these
technological revolutions has a defining characteristics. For the PC, it was
compute, for the internet, it was networking, for AI it's data. And so we're right in the best position to build out this stack and to make it easy
for everybody to adapt. >> So help us understand the data platform because it feels like
you're not directly trying to take on a Databricks and
a lakehouse or a Snowflake. And we heard certainly last
week from Databricks in a lakehouse, we're going up stack. Snowflake is kind of moving some of the function out into
the open source world. So when you say data platform, are you rethinking what that is? >> Yes.
- And would you explain that, please?
Renen Hallak
>> So if you look at older
companies like Databricks or Snowflake, they were
built in the era of big data, analyzing numbers, mainly
columns of a database. And that has a certain scale to it, it has a certain ability to it. It's mainly CPUs doing that analysis. As we shift to deep learning or AI or especially these agentic workloads. We're talking about not
numbers, but pictures and video and sound and genomes and
a lot of unstructured data. We're talking about tens of
thousands, hundreds of thousands of GPUs that are so much hungrier and require that much more parallel access to all of this information. We're talking about needing access to all of your historical data, not just the last two days of information. And so for all of those reasons,
you need a new architecture and you need a new data
platform to do unstructured data to give it structure and meaning, to drive these workloads in a data engine. All of these pieces need to be different for this new world than what those older companies built. 15-
Dave Vellante
>> So another way of thinking
that is you're bringing in a new dimension of context, I would say. Whereas those historical
platforms are historical systems of analytics, what happened
and maybe why it happened, but not really good at
what's going to happen and what's the next best thing to do. Is that the type of context that you feel that you can support? Not that you're actually,
you're not a SaaS vendor. Maybe that's what up the stack,
they're going to do that, but you're going to be the
platform to enable that. Is that the right way to think about it?
Renen Hallak
>> Yeah. When you say up the
stack, I would say you need to start from the bottom
in order to do this, right? The fact that we started
at the bits and bytes and understanding exactly where each piece of information sits on hardware, that's what gives us an edge because well, that's what
makes us the operating system. If we were to start from the top and leverage cloud services underneath us, that would be very different.
Dave Vellante
>> So, John, what's interesting
about this is you see this from Snowflake and Databricks,
they are moving up the stack and they are moving into the domain of Salesforce and ServiceNow. I'm not hearing that from you, Renen. You're not trying to build an ecosystem around the governance layer and try to be the source of metrics and dimensions and do that. You're just trying to be
the best infrastructure and software to enable that to occur.
Dave Vellante
>> That's exactly right.
- So you're not trying to take those up the stack folks on. >> Yeah, I mean this comes up a lot. I want to get your thoughts
on this because what Dave's mentioning is that the old
world, old world, a couple of years ago, two years ago, data analytics drove everything. You mentioned some of the things around how they evaluated the data, and then you mentioned the windows. So you mentioned also KV Cache. So at the layer of
you're at, there's a lot of networking too. So software is now
controlling where things go. You mentioned scheduler events. It sounds like an operating system to me and first principles. So the question is what has
to change in the enterprise or people who think in
a data analytics world because you have disruption and enablement going on at the same time. So you're seeing that
in software development and you're seeing it in sales and marketing kind of
obviously low use cases where AI shining today and agents are kind of
picking that up too. But the analytics in the
data world have the dogma and the old school thinking
around, we built these systems to analyze, to build dashboards. This is not like a cottage industry. This is pretty big and
sizable, huge investments. What do enterprises need to do and what's the connective
tissue that you're dealing with at your layer that's going to enable that transformation from
analytics and intersect and bring in gen AI? Because gen AI is coming in
like a freight train Into the analytics world, and those
people not necessarily aren't platform engineers. They're thinking business logic, UI, application level agents. They're not really thinking
about the plumbing. So as an OS, what do you think about that and what has to happen for the enterprise who are hugging onto their analytics systems like a tree hugger?
Renen Hallak
>> Yes. So there will be
connectors from the old world into the new world. Some people call these MCPs, Salesforce calls it A2A and Google does. It's a way to give agents
access to older information and to data sources that
are already existing. But I think if we think of
this new era as data analytics, we're doing it a
misservice or a disservice. These new agents are the new applications. They're half new applications, they're half artificial people. And so it's a new concept that is emerging and they will do tasks for us. They will go out and study something. They will learn new things. They will come up with
eventually new ideas as they operate on our behalf. And so they'll need access to data, but it's not an analytics workload. It's all of the workloads
when we're talking about legal work or accounting work for the
white collar type of agents, if we're talking about
robotics, it will be everything. Construction, workers,
gardeners, these agents will literally have a life of their own and we need a way for all of
them to communicate, for all of them, to share thoughts
for all of them, to organize their internal and external information. And that's where this- >> So data brings up the
analytics and the governance. I mean the governance piece,
which sits to enable that. How are you guys enabling that? Is it origination of
where the data's stored? Because you're getting closer
to the NVIDIA low-level piece, so you have access to where
the data comes out versus post, it's out there and I'm going
to analyze, it's in a corpus or whatever the format is for analytics. As you get closer to where it's originated, you can see things. There's metadata, now you've
got S3 tables and AWS. You've got Databricks with Iceberg. So now you have a storage
paradigm of how to store it, which is open.
Renen Hallak
>> Governance is super important. I think one of the big reasons why the enterprise
isn't moving faster is because they have
compliance and regulation and other things that
they need to adhere to. For example, access control
lists, who is allowed to see what information? If you're talking an object store and then you start to
vectorize the information, you need the same security
model across your unstructured data and the rows of the database. If you're talking agents,
you need to make sure that this agent is not seeing something that they're not allowed to and that they're not communicating something that they are allowed to see. Especially as we move into this world where we have, today it's all static. It's one AI, it's a
version, whatever it is that OpenAI came out with, and all the agents are
running on the same model. Over time, each of these
agents will start to fine- tune their own model and we will have different AIs
interacting with each other. But that introduces another
challenge for governance because all of the data
that they use to fine- tune is potentially going to leak through interaction with them. And so we need to remember
what that data is. We need to know who is allowed to see it. We need to be able to
observe what happened and where all of these
models came from in order to- >> So knowing the storage, knowing
the origination plays into the knowledge of the lineage.
Renen Hallak
>> All of this goes back to metadata and an understanding of where information-
Dave Vellante
>> So to put a finer point
on, again, where VAST sits, if I'm in the VAST system,
you take care of that. But now above you, there
are other governance, there's Unity, there's Polaris, there's Calibra, there's Alation. So you will surface,
right? But you'll surface. They have access to your
metadata, you will provide that to them so they can then go do their job. They'll hand that off.
Renen Hallak
>> That's exactly right. And
we make sure that it's secure and that it's simple
and that it's real-time and they can focus on
building their applications and building their agents.
Dave Vellante
>> So your TAM expansion strategy is not to go up, it's to go-
Renen Hallak
>> Wide. >> Wide. Yeah.
- Talk about the conversations you're having with customers. You're talking off camera.
I won't say the name of the company, but big financial services, you guys are
talking to everyone. Certainly you had great run
with the hyperscales needed to do business there, but then
you have the next layer of, I call, I won't say tier two, but tier one B, large-scale enterprise, and then you have the
kind of the mid-range. What are some of the conversations
you're having with them? Can you share any stories or
anecdotes around the thinking? Are you whiteboarding stage?
Are they under pressure? Obviously with agents exit,
the complexity increases, but now functionality and
business value increases. So I'm sure you're being
brought into not just technical conversations, but business conversations. >> Yes.
- Share some of those conversations.
Renen Hallak
>> We speak more and more to the AI people
versus the infrastructure people because they are driving the agenda and they have been playing
around for the last year and a half and now they're
realizing the real value and they need to go into production. >> So they're locking in on visibility of use cases and architecture.
Renen Hallak
>> Yes, these big banks
have lists of hundreds of applications that they
need from an AI perspective, and they are in the process
of shifting from playing around in a sandbox to moving
things into production. And at that point, you need
access to all of the data for this to work, and you
need all of the governance and compliance that we
discussed a minute ago. And so it makes it a real
challenge for them to do that with the old infrastructure. And that's why with a
renewed sense of urgency, they're coming to us
and asking us to help. >> What are some of these
with the old infrastructure? Is it latency? Is it the number of hops? Is it speed architecture? What are the key constraints
that they're dealing with?
Renen Hallak
>> It's scale. When you look
at the older workloads, you had a thousand people
in the organization and you needed a million row
database in order to do that. When you think of agents, you're going to have a thousand agents per person. That is much larger scale. When you think of the type of information that now can be analyzed. It was stored before, it was
documents, it was pictures, it was videos, but nobody analyzed it. Nobody knew what to do
with it. Now they can. And so that level of scale and performance that is
required is several orders of magnitude more than what
the old stuff was built for. And that's why- >> They kind of knew what
they wanted to do it but they couldn't do it, right?
Dave Vellante
>> Coming back to your
comment about the sandbox, that sandbox is oftentimes in the cloud and then they bring it on-prem. They're actually doing
some sandboxing on-prem. Oftentimes they don't have,
whether it's the center of excellence or the skills. Now the big banks have
the resources to do that. How do you see that playing out, Renen? Because you've got the big banks, even they are constrained
on power, on liquid cooling. And so how will that evolve? I mean, financial services
tends to be a harbinger of what's coming cloud and
then the financial services and then the rest of the enterprises. There's a big debate, not even a debate, but there's a dissonance
in you don't really need GPUs to do this. Others say, "Hey, inference
is a lot of matrix math. You actually need GPUs."
Renen Hallak
>> You do. >> What's the fact from
a technical perspective?
Renen Hallak
>> You need GPUs, especially for
the first half of inference, what's known as pre-fill. And now we have disaggregated inference. So we're disaggregating
pre-fill from decode, which is basically
understanding the question versus giving the answer. You need a lot of GPUs, especially as we move into these reasoning models where inference is not a one-shot. I'm just going to give you an answer, but it's a multistep process. It's a lot of trying things
out and seeing what works. And so inference is
going to be the majority of the GPU consumption going forward. Training, I would say is something
that you need to do once, you get to a certain baseline
and then inference takes over. I think the big banks are,
as you say, a harbinger. I see three phases. The first phase is playing around in the hyperscalers
and the big clouds. Once they get to a certain level of scale, they realize the big cloud
infrastructure wasn't built for AI. And then they start talking to some of these AI clouds like
CoreWeave and Lambda and Crusoe, and most of them will
sign contracts with those for this intermediate stage
until they're ready to bring it and bring it in-house.
Dave Vellante
>> And what about the fourth wave, which is like everybody else? Are they going to be able to afford that? When does that go mainstream?
Renen Hallak
>> I think everybody else,
as in smaller organizations, will consume AI applications more than build their own agents. And so my guess is very large
technology companies like an Adobe or a ServiceNow
will be the ones that will help them make this transition.
Dave Vellante
>> And that will drive demand for cloud. A lot of those guys
might decide to do some of their own data centers
as well as they already are. >> They already are.
- You're enabling the ecosystem above you. What's your view of that?
Because for you to be successful, you have to enable
which you're talking to, what happens for them? They have to have agents
that talk to each other. You mentioned that. We've
been saying that on theCUBE for a couple of years now, Dave, and so that's happening now. What does that ecosystem look like and what are some of the requirements for success from your opinion
around what that looks like? Partnerships are changing. You mentioned MCP that's
kind of creating kind of this API vibe around, Hey,
we can now talk to each other. What has to happen? What do
you need to do to enable that? And what does the ecosystem
above you need to look like?
Renen Hallak
>> Yeah, so if we are going
to be the operating system, we need to partner with
application developers on top of us or agent builders on top of us as well as the infrastructure people underneath. And so we talked about partnering
with NVIDIA, partnering with these clouds, partnering
with companies like enterprise in order to make our
way into the enterprise. But we really want to
enable agent builders. And so what we're now starting to do is write our own agents. To just give examples,
they're all open source and we can show how easy it is to do that.
Renen Hallak
>>
- We are. >> So I have to ask you,
this comes up a lot. We certainly debate on
the queue part every Friday pretty much at this point. Is agents going to replace SaaS or is that an evolution in your mind? Because there's a lot of
SaaS companies out there.
Dave Vellante
>> Agents talking to CRUD
databases, or is that clippy? >> Well, I mean there's
an architectural shift, but I mean, the cloud era
proved that SaaS could work and got app stores, and
we all know the history there don't need to revisit it. But now agents are going to hit
the scene with huge numbers. Is that an extension in evolution of SaaS or is that kind of a wholesale
replacement in your mind?
Renen Hallak
>> I think it depends on the
company, whoever they are. If they make this transition and start to take advantage
of these new abilities, then they'll be fine. And if they stay where
they are very quickly, they will be over-
Dave Vellante
>> And they have some real advantages in that they have the data, the metadata, the business logic, the process logic. And so if they lean in, you
would think they'll be able to at least maintain their franchise. You'll see how far they can
go. When we get a technical visionary like yourself,
I want to ask you about, you mentioned MCP and A2A. My understanding is that MCP
is stateless A2A is state. A lot of people think it's
like competing standards, maybe it is and eventually
will become that. How do you think about those
two emerging standards, because both seem to have momentum.
Renen Hallak
>> Yeah, it's early days. I never make a bet on one
standard versus another. I look at the concepts behind
it and the ideas behind it. I think the idea of both
of these standards is to put some type of structure
formalization behind how agents communicate. I think it's super important
not to confuse agents with web services, which
I've seen a few people do as a result of these types of standards. I think agents are long-lived things. They have long-term memory. They have experiences, especially as we move into these
physical agents and robots. They have sensors that
take in pictures and sound, and maybe one day somebody will
develop a good smell sensor and they develop thoughts. And so it's unstructured data coming in. It's understanding what's in
it, it's giving its structure, and it's comparing that to old thoughts and old memories as we generate new ideas.
Dave Vellante
>> As opposed to hard-coded sort of microservices that are static. This is an organic system.
Renen Hallak
>> It should live. And in some sense, the vast operating system
should be the habitat for these new organs. >> Yeah. Jensen made a
controversial comment, Dave, we obviously covered it
at CES when he said, " It will be the HR department
for agents tongue in cheek," but he's actually not wrong. So what came out of the
Databricks event was the focus on evaluation where you
have reinforced learning with human feedback, but the human feedback is being
statistically calculated as you're managing your kid. How'd you do in school
today? How are your grades? So this supervision of
agents going into the pre- agent evaluation before they're deployed. So that's almost like an HR department, like, Hey, how'd you do?
Renen Hallak
>> I think he's right. >> What's your thoughts
on this? Because this is where we're seeing the math and the computer science really
shine on agent deployment. Evaluate first, understand the task, agents supporting agents, supervisor. And we even had Augment on and some code companies
talk about the fact that you have human supervision where the developer becomes the tech lead. So the code's being generated by machines. So this idea of supervised,
kind of an HR department.
Renen Hallak
>> It is, and I wouldn't
call it HR, it would be AR. But yes, I think- >> Agent relations. >> Resources.
- Resource agent resources.
Renen Hallak
>> I think humans have a really
important role in teaching agents what we know. And then once this
reinforced learning happens, I think it takes a life of its own. It's not just reinforced
learning by human feedback. It's reinforced learning
by agent to agent feedback. It's reinforced learning by interacting with the natural world
through these sensors. And if you really can over time, fine tune your individual model, each of these agents will have a
different model of the universe. And that's how ideas- >> Yeah, you're enabling this. And I wanted to bring it
up because one, it was fun to reference the Jensen thing,
but in the computer science world, this is real science going on. This isn't just random evaluation. So folks who are afraid agents,
agents will go off the rails and turn into Terminator,
do some weird things, ship code into production
that's not supposed to be there. So this is where, can you
share your thoughts on why the computer science is important
behind this one point?
Renen Hallak
>> Well, I think that's where
the security aspects come in, and we have to be in
control as much as possible. But more than that, we have
to know what's going on. We have to be able to
observe communication. We have to be able to look back in time and see what happened. All of these things come back
to a persistent layer that captures all of this information and for us to be able to read that information in an easy way. And so we need to make sure that it doesn't go off the rails, but there's a lot of good
things that can happen by us giving them a little bit of freedom. >> Thank you so much for coming on. Again, you're a leader
in the AI infrastructure and obviously that impacts
robotics, which is an edge. We didn't talk much about
that, but continue to nail that data platform, that
operating system, getting close to the hardware, orchestrating
scheduling, managing events, originating the data, all part of the big wave. We're
on. Thanks for coming in. >> Thank you.
- Appreciate it. Okay. The co-founder of Vast Data
is in the house here really sharing his thoughts on
the enablement required to bring in the agent
era that we're seeing, which interests more
value, but complexity. And if you get the data in control, the infrastructure will do great. This is theCUBE bringing all the action. I'm John Furrier with Dave
Vellante. Thanks for watching.