In this segment from theCUBE + NYSE Wired’s “AI Factories – Data Centers of the Future” series, theCUBE’s Dave Vellante sits down with Rob Biederman, managing partner at Asymmetric Capital, to unpack a disciplined approach to early-stage investing amid AI-scale infrastructure shifts. Biederman explains Asymmetric’s founder-first model: writing $1–$10M checks (often via SAFEs), joining boards as they form and helping operators with go-to-market, operations, finance and strategy (not product/engineering). He shares why the firm avoided 2021’s lofty SaaS multiples in favor of backing proven builders earlier (single-digit pre-money), and highlights portfolio execution such as a cash-efficient LATAM e-commerce company scaling from ~$1-2M to about $50M in revenue. The discussion also explores Asymmetric’s subscale buy-and-build plays (e.g., pool cleaning in San Diego, sleep apnea clinics in Houston), where density, tech-enabled services and platform ops expand margins and enterprise value.
Biederman weighs in on AI economics as enterprises race to “AI factories,” cautioning that not every AI workload creates ROI and that overbuilt compute assumptions could face a reckoning. He argues that winners will prove a clear 10× value equation and avoid scaling go-to-market before product-market fit. Additional insights include early liquidity discipline (returning $0.20 on the dollar before the fund’s third anniversary), portfolio survivability (34 of 35 companies still operating; three positive exits), and guidance to founders: make your value proposition relevant, credible and differentiated. Tune in for candid perspective on how capital efficiency, ownership discipline and anti-thematic sourcing intersect with a world where GPU-dense data centers and AI-scale software are reshaping enterprise infrastructure and economics.
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Felix Ejeckam, Akash Systems
In this segment from theCUBE + NYSE Wired’s “AI Factories – Data Centers of the Future” series, theCUBE’s Dave Vellante sits down with Rob Biederman, managing partner at Asymmetric Capital, to unpack a disciplined approach to early-stage investing amid AI-scale infrastructure shifts. Biederman explains Asymmetric’s founder-first model: writing $1–$10M checks (often via SAFEs), joining boards as they form and helping operators with go-to-market, operations, finance and strategy (not product/engineering). He shares why the firm avoided 2021’s lofty SaaS multiples in favor of backing proven builders earlier (single-digit pre-money), and highlights portfolio execution such as a cash-efficient LATAM e-commerce company scaling from ~$1-2M to about $50M in revenue. The discussion also explores Asymmetric’s subscale buy-and-build plays (e.g., pool cleaning in San Diego, sleep apnea clinics in Houston), where density, tech-enabled services and platform ops expand margins and enterprise value.
Biederman weighs in on AI economics as enterprises race to “AI factories,” cautioning that not every AI workload creates ROI and that overbuilt compute assumptions could face a reckoning. He argues that winners will prove a clear 10× value equation and avoid scaling go-to-market before product-market fit. Additional insights include early liquidity discipline (returning $0.20 on the dollar before the fund’s third anniversary), portfolio survivability (34 of 35 companies still operating; three positive exits), and guidance to founders: make your value proposition relevant, credible and differentiated. Tune in for candid perspective on how capital efficiency, ownership discipline and anti-thematic sourcing intersect with a world where GPU-dense data centers and AI-scale software are reshaping enterprise infrastructure and economics.
In this interview from theCUBE + NYSE Wired: AI Factories - Data Centers of the Future, Felix Ejeckam, chief executive officer and co-founder of Akash Systems, joins theCUBE and NYSE Wired's Gemma Allen to discuss how synthetic diamond cooling is turning the data center energy crisis into an efficiency opportunity. Ejeckam explains how Akash applies lab-grown diamond to GPUs inside servers, reducing chip temperatures by 10 to 15 degrees Celsius and unlocking roughly $1 million in additional value per server for operators. Originally proven aboard half a dozen...Read more
exploreKeep Exploring
What is Akash Systems' Diamond Cooling technology, how did the company pivot from space (satellite radios) to data centers, and what cooling, energy-efficiency, and economic benefits does it provide for GPUs/servers?add
Which markets do you sell into?add
How is a California-based diamond-cooling company structured and operating—its team size, geographic distribution, partnerships, sales cycle, funding, roadmap, industry relationships, and R&D approach—in order to address global data‑center energy and resource constraints?add
>> Welcome back to TheCUBE Studio, coming to you here from the New York Stock Exchange. This is AI Factories, one of our segments with NYSC Wired. And joining me now is Felix Ejeckam, CEO and co-founder at Akash Systems. Welcome, Felix.
Felix Ejeckam
>> Welcome. Thank you. Nice to meet you.
Gemma Allen
>> So it's a very wet day here in New York, Felix. I hope you have better weather where you are right now in California.
Felix Ejeckam
>> We do. It's a little bit warmer here and drier in San Francisco.
Gemma Allen
>> Well, that's good. So to start, maybe if you can take me through an explainer of Akash Systems, break down what exactly is GaN-on-Diamond for a smart high schooler?
Felix Ejeckam
>> Sure, so Akash Systems is a deep tech company based in the Bay Area that has solved the heat problem in data centers and AI. Your listeners have probably heard untold stories of the challenges with the limited supply of energy in the data center world. And not only is the limited supply of energy a problem, but also the efficient use of that energy is a major challenge. At Akash Systems, what we have done is use the world's most thermally conductive material, diamond, to solve that problem so that existing users and new users can efficiently use their energy for their needs in compute.
Gemma Allen
>> Wow.
Felix Ejeckam
>> So more specifically ... Yeah, go ahead.
Gemma Allen
>> And I believe that this company has had, I guess, a starter use case and somewhat of a shift in terms of the roadmap on its broader application. Talk to me a little bit about that. You started in the space field and now have had a pivot. What was that?
Felix Ejeckam
>> Yes, that is correct. So we started using Diamond Cooling, which is the name of our technology, in space. We have been making satellite radios for the use in telecommunications in space. And as of last year, we have half a dozen satellites in orbit and they're still operational today. And it was two years ago when we realized, wait a minute, if we can address the heat problem in space, which is a more challenging environment, then we can definitely address it on land, on the earth. And that's what we proceeded to do. And so today we take diamonds, synthetic diamond, lab-grown diamonds, and we apply it to the GPU in a server. The GPU is the hottest chip in a typical server and that brings a temperature down. The application of the diamond brings it down by 10, 15 degrees Celsius. That then leads to opportunities. The fact that you don't need as much power to cool down that server, we take that extra power and we can give it to the operator either in savings or in additional computes that you can fill back into that data center. And typically speaking, we're talking about a million dollars is how much our technology gives to every operator of a server per server.
Gemma Allen
>> Wow. Wow, those are interesting margins. And talk to me about the recipient of that value. What is a typical buyer use case scenario? Talk me through a tactical example, I guess, of how this is used.
Felix Ejeckam
>> Sure. There are three markets that we sell into. The first is the AI inference company, the company that's either training models or carrying out the inference work. A lot of the neoclouds fall into this category. A lot of the big tech companies that you know about fall into this group. The second market is the data center market, so these are folks who buy servers and operate these servers for their customers. The third is the cloud service provider. These are folks who sell time on their GPU. This is a little bit of the rent a GPU model. And so these three markets are the folks who buy our servers. And these are diamond cool servers and they would be the recipients of the $1 million per server that I just described.
Gemma Allen
>> I mean, those each individually are monstrous markets, right? In terms of the overall market value and also what's happening right now in this kind of tech revolution that we're on the cusp of, if not in the midst of. So let's start with inference because that's an interesting topic. And we, on this AI Factories show, obviously we have the big incumbents, right? We don't need to name them or the one. We don't need to name them or him, but we have a lot of folks coming in who are very enthusiastic about the opportunity to make ground as a GPU provider in the inference era. What are your thoughts on that? What are you seeing from the perspective of competitive dynamics?
Felix Ejeckam
>> Competitive dynamics, to be very frank, we don't see a lot of competitors in our space for what we do. The innovation in this world has been fairly limited in recent years. The number of companies offering servers has been approximately the same over the last decade. Our technology, our company is the first new entrance in a very long time and so it's a little bit of a revolution. Yesterday we introduced, for the first time, the world's first diamond cooled server using an AMD chip. No one has ever, ever done that before and so we're very excited about that. We think that that boats well for the stack. It'll help the market accelerate growth. It brings a lot of new parties into the fray because there are a lot of companies that need to address this heat problem for them to serve their clients. A lot of companies have fixed supply of energy to their data center. We help them bring in the revenue from 2027, 2028 into 2026 because we're giving them efficient use of their electricity.
Gemma Allen
>> And the data center space, which is again, I mean, a hotly controversial and well conversed market, we hear a lot about the hyperscaler debate, what's happening in that space broadly. Some of the challenges we're also seeing here even in terms of the AI war, right? Or the race to the top for AI from the perspective of even just this week we had some interesting news around some big tech titans in the White House talking about energy costs and protecting consumers, et cetera. So it seems as though the space you're in, it's a hot, hot space, right? It's definitely extremely futuristic. Talk to me about what you're seeing from the perspective of challenges that your customers are facing. Is it energy? Is it compute capacity? Is it generic constraint here in the US?
Felix Ejeckam
>> Gemma, it is ultimately energy. The compute problem is an energy problem. There is a fixed supply of energy in the world and so there is a mad dash rush to go and grab that energy and make the best of it. What we offer plays right into that story. We're saying to participants in the marketplace, "You've got a fixed amount of energy for your data center, a megawatt, a gigawatt, whatever. Rather than go build another gigawatt plant, how about we help you double the capacity in that existing infrastructure?" So we will let you double your compute, for example, using our diamond cooled technology. So I think your points are exactly correct. There is limited supply and limited use of energy in the world, in data centers in particular, and it is incumbent upon companies like us to try and enable the actors in the space to use their energy as efficiently as possible. And that's what diamond cooling is all about.
Gemma Allen
>> So companies based in California, I think you mentioned off camera you're at about 25 people, so relatively lean, which seems to be the norm actually in this current era we're in, right? Talk to me a little bit about the geographical distribution. This is obviously a very global space though, right? Data centers and the kind of broader picture in the space has a lot of supply constraint challenges the world over. How are you thinking about that? How are you approaching it?
Felix Ejeckam
>> So we are largely California based, although that will change in the coming year. As you may know, we announced a new deal with our partners, AMD and MiTAC Computing for a $300 million contract to support data centers in the market with our diamond cooled servers and using AMD's chip, the intrinsic 350 and MiTAC Computing's abilities to make assemblies. We're going to be doing this across California, which is where we operate. Initially, we're serving largely clients in North America. We do anticipate that that will grow in the years ahead, but we're largely Bay Area focused. The problem, however, is global, absolutely is global, and that's a problem of limited resources, energy, limited water, and the like. So yeah, it is a very significant problem that we see all over the world. Let me say that a week ago we announced release of an Nvidia based server. Again, another diamond cooled server. This is the H200 and that was for a customer in India, so we have been receiving inbound requests from all over the globe.
Gemma Allen
>> And what's the sales cycle like, Felix? How long is it from conversation to sell for an industry and a market like this? I assume it's moving much faster than it was.
Felix Ejeckam
>> It's a lot faster than even just two years ago. We see cycles that spanned 60 days, but it can also go as long as nine months. Forgive me. So it is still fairly lengthy, but today it is a lot shorter than it was, let's say, two years ago from what we see from the inbounds requests that we're getting from customers.
Gemma Allen
>> So you've raised $60 million to date and you've had a series B, which closed quite recently?
Felix Ejeckam
>> Yes, we closed a series B last summer. Our leading investors in the company are Coastal Ventures and Founders Fund. We have a host of other investors, institutionals in the company, and we're very excited. We have the capital that we need to scale, which is our focus this year.
Gemma Allen
>> And you mentioned some kind of geographical opportunity perhaps ahead, but what's on the broader roadmap for the next 12 months?
Felix Ejeckam
>> The broader roadmap, Gemma, is scaling, scaling, scaling, scaling. And then we're going to be adding more SKUs, doing more with our partners at AMD and Nvidia. They, as you know, have a lot of different GPUs. We're going to be cooling all of them with our diamond cooling technology. Of course we have our partnerships with the OEMs with in particular MiTAC Computing, which is going to deepen as we scale over the course of the coming year.
Gemma Allen
>> Great. So we mentioned off camera that we're both heading to San Jose the week after next Nvidia GTC.
Felix Ejeckam
>> Absolutely.
Gemma Allen
>> They're obviously a huge, huge player in the industry. Talk to me a little bit about the collegiality in the space right now.
Felix Ejeckam
>> I'm sorry, the what?
Gemma Allen
>> Sorry, the collegiality, the community, the, I guess, partnership and friendships that are forming across an industry that is so hotly competitive.
Felix Ejeckam
>> This is an industry where I like to use the word frenemies. There are no enemies. There are no besties. There are just frenemies and folks with common interests and that's what we've seen. We've seen this before in the other industries that we came from, but here in particular there's definitely a very good camaraderie amongst all of the participants. And the simple reason is that the opportunity for all of us is so big. It's much greater than the capacity or the capability of any one company. And so it is incumbent upon all of us to work together to try to fulfill that need.
Gemma Allen
>> And lastly, talk a little bit about the R&D cycle. So we talked about very, very fast sales cycles. Things are happening so quickly. It's very reactive because it needs to be to meet market demand. On the R&D side, though, it's also highly complex. How do those two challenges meet?
Felix Ejeckam
>> Yeah, it's complex, but you hit the nail on the head. You not only have to solve difficult problems in R&D, in this industry you have to do it rapidly because new product cycles are coming online rapidly from everyone in the ecosystem and we have to keep pace with that and, in many instances, get ahead of it. It's one of the reasons we stay lean. It's a lot easier to go faster than not if you are smaller. If you have a very large R&D team, then it's more difficult to get everyone aligned. So you do have to innovate rapidly and the pace that I see today is unlike anything I've ever seen in my career. It's definitely fast.
Gemma Allen
>> So lean team of 25 people, but when you are hiring, what are you hiring for? What kind of culture are you building at Akash?
Felix Ejeckam
>> Gemma, we're hiring right now. We're looking for people, we're looking for engineers, we're looking for R&D folks, we're looking for manufacturing folks, we're looking folks at the back office, and just we're looking for talent wherever we can find them. There's definitely limited supply of talent in the Bay Area, but we're scaling and we're finding the right people that we need. As I said, engineering for both R&D and manufacturing.
Gemma Allen
>> Well, Felix, wonderful to chat to you. Fascinating company, fascinating space. Hopefully, maybe catch you in San Jose in two weeks.
Felix Ejeckam
>> Thank you, Gemma. I'm sure I'll see you.
Gemma Allen
>> Absolutely. Thanks so much for coming on TheCUBE.
Felix Ejeckam
>> Thank you. Take care.
Gemma Allen
>> I'm Gemma Allen coming to you from TheCUBE Studio here at the New York Stock Exchange. This is AI Factories, one of our programs with NYSE Wired. Thanks so much for watching.
Gemma Allen