Hello, I'm John Furrier with theCUBE. We are here at theCUBE Studios on the East Coast at the NYSC, New York Stock Exchange. We got a great guest here and the mixture of experts in our AI factories. We break down and talk to the leaders making it happen. Sid is here. He's the founder and CEO of d-Matrix. Welcome back to theCUBE. Great to see you. It's like our sixth time. You've been on so many times.
It's been many times. I'm so happy to be back.
Well, a lot to talk about. I want to get into kind of the market activity around the tech, the business model you guys have and updates. But first, big news, you guys got some fresh funding. Love to see these big rounds. Congratulations. A Series C. Tell us about it.
Yes, so we just announced our Series C round of funding, which was a $275 million round at a $2 billion valuation for the company. So good outcome for the company, and this brings our total funding to about $450 million cumulative. And well, that gives us plenty of dry powder to go out and do the things that we want to do and service the massive opportunity in front of us.
I'm just curious, what's the valuation?
It's going to be $2 billion. Yeah.
$2 billion. Not bad. Double unicorn.
What's the use of funds? What's the thinking in new investor share? Who's participating?
Yeah, so this round was led by a consortium. We were fortunate. We had a global interest in this round of funding. This was co-led by three investors: Bullhound Capital from the UK, Triatomic Capital from Silicon Valley, and then we had Temasek join the round in co-lead this round from Singapore. And we have had other investors like Qatar Investment Authority, QIA, and EDBI from Singapore also joined this round, besides having the old investor like Microsoft and others participate in this round.
Well, congratulations. It's great validation.
Again, we've been following you for a long time and you made a big bet and inference is paying off, so congratulations.
Thank you. No, thank you. It's been... Very excited about what the future holds.
We're in the hottest area right now in infrastructure. I was just talking to the folks over at GSA. They're having their awards coming up in December. It's sold out and the waiting list is a mile long, not like the semiconductor industry. It's like now supercomputing, the show started in 1998 when I graduated college, still around. It's hanging around doing high performance computing. Now it's essentially a supercomputing show, where actually supercomputing is actually happening.
It's an AI show. We're planning for NRF, which is a retail event that's now an AI show. Everything is an AI show and the AI infrastructure is making it all happen. Clearly the stock market here is recognizing that, but I want to get your thoughts on where you see this now because that's clear. But what that's enabling is the scale for data and you're starting to see this agentic layer and then the next path after. That's the physical AI where the full convergence of physical and digital come together in one thing. So as we're in this AI build out on the infrastructure side, there's now this next level that it's going to enable. What's your reaction to that? What's your view?
Well, I think the big revelation or the big understanding that people are finally getting to is inference, which is something we've been talking about for, ever since we started the company. The company was built on a foundational principle that inference is going to be the largest computing opportunity of our time. It's a generational opportunity and I think people are finally coming to that realization and every time they try to put a target on the amount of compute that will be needed to serve all of humanity, I think it just surprises them that they need a lot more than what they originally anticipated on.
Yes. And siliconangle.com, we've been featuring a lot of the content around this, the word silicon, Silicon Valley, the hardware game of silicon has become more prominent and help people understand some of the state-of-the-art technology because clearly we talk about hardware and software. We're not a hardware company, we're a software company, but silicon and software kind of come together. There's still coding involved.
What's the real advancement for the folks that aren't in the details of the latest silicon advancements? What's the current state of the innovation?
Yeah, I think the key thing is I think we are moving into a world that is going to be heterogeneous where you're going to not have just one class of compute dominating all the infrastructure build outs that are happening right now. So I think you're going to have different forms of compute. I think it's not a one size fits all world that we are moving into. We are moving into a world where inference is going to be used by all of humanity, and what that really means is you need different strokes for different folks. You're going to need different types of compute depending on the application. You're going to need different types of compute depending on what the constraints are on the application. Is this something that requires low latency? Is this something that requires, needs to be energy efficient? Is this something that needs to be more throughput oriented? Is this something that is just very cost sensitive? So I mean you have all these different constraints that need to come together and how do you build a single platform or single type of computing solution that addresses all of these diverse needs? Very difficult. So we are moving into a heterogeneous world where I think you'll see coexistence of different forms of compute, and now when you enter that world, this is different types of architectural solutions, which basically means anyone who is writing software really needs to understand the underlying infrastructure that they're writing the software for. It's not like a unifying computing solution like we had in the CPU age where you had one CPU, it was x86, you went and wrote software, the software ecosystem was very mature, so the application level developers really didn't need to worry about what the underlying hardware really looked like. I mean, it was already prepared for them. Now it's changing. It's like you're seeing a very quick and dynamic shift that is happening in the underlying infra, the underlying compute, which basically means if you want to write applications for that type of infra, you really need application developers and programmers to understand what the underlying infra looks like and how to program for it.
So I want to get your thoughts on the concept we've been tracking around distributed computing, going AI and networking. We were talking before we came on camera. I'm working on this big hyperconverged edge report for Mobile World Congress. Just two weeks ago at the NVIDIA GTC in Washington D.C., Jensen Huang keeps us amplifying this concept of extreme co-design. Now that's his marketing term for we co-design with our partners and that's how systems people work. Okay, that's not the question that's kind of sets the table. If you look at distributed computing of edge, AI factories, large cluster supercomputers working together, there's a diversity of devices and there's diversity of development kits or environments. The role of silicon will be super important because you might have a wearable, there could be a Wi-Fi access point or a converged 5, 6G with WLAN, Wi-Fi device with a smart DGX box.
That requires... That little device has to talk, it's going to have some horsepower but not a lot, and it has to talk to the big factory or other servers and cloud or whatever it does. That requires that the software has to run across those different diverse form factors, maybe different memory constraints. You guys have been doing a lot of work around memory, the memory wall. I think last time we talked, you talked about memory wall.
It feels like it's only going to get worse, like when you think about a seamless entity, I'm a user, I'm at an edge, I'm in store, I'm on the road, I'm driving in my car and I want to go back and get a model that an agent does for me. It's got to do it really, really fast. It means that the silicon has to work in that system. What is your reaction to that? What's your vision? Because that's going to require really smart silicon, really smart software, really smart system architects, almost a co-design thinking.
Yeah, no, absolutely. And that goes back to my previous comments, which is, this is we are entering a world where there's different forms of compute, it's heterogeneous, they all need to co-exist. Applications being developed need to be developed for different types of underlying hardware, right. So I think at d-Matrix, we looked at the inferencing problem and quickly concluded that, it is really, the core problem here is about getting access to data very quickly. Because as you said, everyone wants to do fast inference, everyone wants to do real-time inference. You want to make quick decisions and act on them, right. And how do you do that when there is so much data sitting out there, all distributed in so many different places? You need to bring that together and quickly, and then act on it, make decisions, act on it fast. So I think the inferencing problem, which is where most of this stuff happens, is really a problem where you are trying to bring compute and memory as close together in a very creative way. And that is really what the D-Matrix solution is all about, is how do we put these two things together and break through that memory wall.
Give an update on the business. What's the current momentum? Can you share some of the successes you've had since last time we talked? What's the forward progress look like?
Right. So I think last time when we spoke, which was six months ago, we had the product out in the market. And so we are making some tremendous progress because now the product is being piloted by multiple different customers. We are kind of getting into the phase where we are now not a pre-product company anymore. So the company is going through this transformation where we are moving from a pre-product phase to a post-product phase, lots of pilots, and we are in the process of scaling the business across multiple customers, scaling the business across multiple products. Because most of these customers, they don't want to see just one product. They want to see, 'Okay, this is great. You have a great product today, but how does this evolve?' So we are in the process of building out a roadmap and yeah, super excited about scaling this across different opportunities.
What's the business driver for you right now? Just the build out of AI infrastructure? What's the main tailwind?
It is. It is absolutely that. So it is like everybody wants inference. Everybody has realized that this is where we make an ROI on the investments that we make. This is where we use AI. This is where we get access to all that AI promise that we all talk about. So we have so much customer interest right now. For the company today, it is really about scaling into all of that customer interest and managing that and making sure we don't upset anybody, right. So it's about carefully managing...
effectively. That's right.
