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“As Jensen Huang of NVIDIA put it, it was a match made in heaven,” said Rich Whitmore, President and CEO of Motivair by Schneider Electric, sitting across the table from me at a brewery in Buffalo, New York.

He was referring to Schneider Electric’s purchase of 75% of his liquid cooling company for $850M cash in February 2025. (The remaining 25% is set to follow in 2028.)

“We were under (Schneider Electric’s) watchful eye and we didn’t know it,” he added. “I think they had identified us as an important part of their product portfolio. They acquire best of breed, the elite. We were just kind of doing our thing and growing our business.”

Rich smiled, quick to pivot the credit for Motivair’s success to his father, its founder. Graham Whitmore emigrated from Europe to Buffalo in 1977 to open the U.S. factory of the cooling company that employed him. He founded Motivair eleven years later, in 1988. Its original focus was manufacturing industrial refrigeration and process cooling systems, but the firm evolved over the decades into a prominent global leader in high-efficiency liquid cooling and thermal management for AI and high-performance computing (HPC).

Today, Motivair by Schneider Electric’s technology is installed in six of the top 10 supercomputers in the world, including the three fastest in the U.S.: Aurora, Frontier, and El Capitan. El Capitan is currently the fastest supercomputer on Earth.

“It wasn’t really me, it was really my father,” Rich said, noting that Graham passed away in 2015. “He was like the American dream. Came over here with nothing, literally nothing, no family, no relatives, just he and my mother. They built this company from the ground up, no debt, never took a loan from the bank. They were not wealthy people. I grew up in a very humble household and I’m just privileged to still be connected to this business. I love what I do.”

‍A Media Tour

Earlier that day, Rich led a group of 31 journalists, including myself, on tours through Motivair by Schneider Electric’s headquarters and a manufacturing facility. We got an up-close-and-personal look at their chillers, their heat rejection technology, and we saw the end-to-end assembly and testing of one of their Coolant Distribution Units (CDUs), which reminded me of the size of a skinny vending machine. We also got a look at the inner workings and assembly of their ChilledDoor, a kind of door-slash-refrigeration unit that delivers scalable cooling directly at the rack level.

So there we were, Rich and me, sitting at a table on the second floor of a Buffalo wing joint-slash-brewery for a 30-minute interview after a long day of touring. All around us, other Schneider execs chatted with reporters from around the globe. Downstairs, tables full of journalists snacked on nachos and Buffalo wings, drank Diet Pepsi, and tap, tap, tapped on their keyboards, transferring their scribbled notes from the tour that day into their laptops.

Upon sitting down, I met Rich and his PR rep and let them both know that I’d done some research and I wanted to use this time to interview him for a profile. The look on his face said it all; he wasn’t expecting that. He graciously agreed, and our conversation continued.

Silicon Valley, Austin, and… Buffalo?

When Graham and his wife, Sylvia, settled in Buffalo in 1977, they quickly recognized that the city was rich in resources: sheet metal work, engineering, and thermal transfer.

“There’s several heat exchanger companies that are based here. But the best resource in this town is the people,” Rich said. “Hardworking people, salt of the earth, that we could not have built our business without. Buffalo is a special place.”

Asked what makes it special, he said Buffalo “has all the great features of a big city in a small, close-knit community where people generally get along."

A Father’s Tough Love: ‘Sink or Swim’

Rich studied mechanical engineering at Rochester Institute of Technology with a focus on heat transfer.

In the early 2000s, as a young mechanical engineer, Rich expressed interest in working for the family business. Graham’s answer was pragmatic.

“He said, ‘Look, to be fair, I’d rather have you go and cut your teeth somewhere else rather than on my dime. But there’s an opportunity here.’”

It was a sales management position.

“My father told me, ‘I’m happy to give you this opportunity, but it’s sink or swim. You can’t be here and not be great at what you do because you carry my last name, and that’s it. If it doesn’t work out here, I still love you. You can go and work somewhere else.’”

“Fortunately for me, I think I turned out doing okay,” Rich said.

Purpose, Conviction, & the Cray Years

The mechanical engineer in Rich likes to see how things work. Having watched his father grow Motivair Corporation from the time he was a child, he knew a little bit of what he was signing up for when he joined the company.

“So, my father actually emigrated to the United States in 1977 to start a business in the U.S., but he was working for a company,” Rich said. “He wanted to open up a factory in the U.S., and it was for computer room air conditioning. And that’s what he’d done his whole career. He was young at the time. And when he started Motivair originally, he said, ‘I don’t want to be in the computer room air conditioning business anymore.’”

When Rich joined the business, Motivair was doing a lot of critical process cooling. They had some data center work, but also hospitals, factories, anything that needed to operate 24/7.

“Around the time of the dot-com boom, we started getting drawn into the data center industry,” he said. “So, I’ve been kind of a part of this evolution for really my whole career. The world was a lot different then. Data centers were a lot smaller. There was always a level of criticality, as you can imagine. But over the course of my career, because of the level of resiliency and redundancy that we were producing with our products, like this robust nature design, we were very quickly drawn into some very critical cooling applications in data centers. So, it didn’t take long for me to start getting these gray hairs coming through.”

A Decade of Arduous Work Coming to Fruition

Modern liquid cooling technology like Motivair’s sits as a sort of sidecar to GPUs, both to chill and to reject heat. In large part, this tech was developed by Motivair in collaboration with leading supercomputer manufacturers, Rich said.

“What you’re seeing commercialized now, a lot of that hard work was really done more than a decade ago, and we were right smack in the middle of that,” Rich said. “Cray was acquired by HPE, but Cray was the innovator and they invented the first supercomputers. Those systems that we developed for them were cooling computers that were harder to develop than putting somebody on the moon."

Knowing what he was working on was making a difference gave Rich a deep sense of purpose, he said.

“Nobody had been doing this at the levels that we’d been doing it, nobody,” he added.

A Different Approach to Talking About the Tech

The unassuming CEO has a unique way of translating tech jargon into plain English.

“We don’t make power, we just move it,” he explained. “The power comes into the data center, and 100% of that power that goes into that server is converted to heat. If you have a gigawatt campus, for example, okay, there’s some power that goes to lighting and desktops and things like that. But the vast majority of that power goes into the servers. They create tokens, and the output, the result, is heat.”

When Motivair engages with a customer, staffers can speak knowledgeably and with credibility about any part of the project that they’re working on, Rich said.

“We speak this language fluently. It makes us hugely valuable to customers, regardless if they’re buying everything in the (Schneider Electric) portfolio or just parts of it.”

During the cooling panel earlier that day, Rich described Motivair’s role with customers as “the adult in the room,” the engineer guiding hyperscalers and AI factory developers onto a safe path through liquid cooling deployments most have never attempted at scale. Motivair was cooling 400-kilowatt racks ten years ago. Today’s industry-leading commercial deployments run 150 to 200 kilowatts. The company has been ahead of the heat for longer than most of its customers have been in the data center business.

A Beautiful Match on Both Sides

When Motivair was acquired, the company had its choice of suitors.

“We could have chosen a dozen other companies,” he said. “We chose Schneider because we saw where they were in the industry, and, more importantly, we felt that their culture aligned with ours. They had a commitment to people, a commitment to innovation.”

Rich stayed on as CEO and the company headquarters remain in Buffalo.

“This was a highly strategic move,” he said. “This liquid cooling expertise was the one thing that Schneider was missing. And so by acquiring us, there is no other company that has this portfolio of products. And it doesn’t mean that we roll into every customer and force-feed them this end-to-end solution. Yes, we’ve got very large customers that benefit from that, but it means that when we engage with a customer, we can speak knowledgeably and with credibility about any part of the project that they’re working on. And even if they’re not using Schneider product X, Y, or Z, we understand what they’re using and how it can impact Motivair’s cooling systems that are there, or the chiller plants that are outside.”

A Ticket to Scale

For Motivair Corp., Schneider Electric brought something the company could never have built quickly: scale.

“We’ve been very, very pleased with the integration and becoming part of Schneider,” Rich said. “We benefit from the vast resources of Schneider Electric, supply chain expertise, global factories. What we’ve been able to do is start taking our products and industrializing them so they can be built in other Schneider Electric factories. We actually have footprints in a large factory in Bangalore, India, and the cooling factory outside of Venice, Italy in a town called Conselve. So there’s actual Motivair product rolling off those lines today to support different regions.”

Growth and the AI Buildout

I’d wager that no one realized, even at the beginning of 2025, how rapidly AI would advance from training to inference to agentic, or how much cash the hyperscalers would start to dump into new data centers and AI factories to meet the dizzying demand.

Goldman Sachs’ comprehensive structural modeling of the AI buildout anchors baseline global AI CapEx at $765 billion for 2026. The Big Five are projecting capital expenditures of $660 billion to $690 billion this year alone, with Amazon expected to spend $200 billion on a massive footprint expansion, and Alphabet more than doubling its 2025 spend to roughly $175 billion to secure its generative AI position.

The boom is unprecedented, and the nascent partnership between Schneider Electric and Motivair is clearly yielding dividends. I had the opportunity to see this very thing coming to life the day after Rich and I chatted in the brewery, when our same group of 31 journalists boarded a bus for an hour-long drive to tour a hyperscale-tier AI campus that Motivair cooling is heading into. (More coming on that tour on Tuesday, May 26.)

We donned yellow vests, hard hats, and protective goggles and navigated rocky terrain between two partially constructed, mirror-image data buildings, each one about the size of two Costcos glued together. The first steel pipe on the older of the two went up six months ago; on the newer, two months ago. Cool breeze from the adjacent lake kicked up dust along the pathway. Cranes towered above. Tractors scooted here and there. Steel beams reached skyward. Thick multicolored electrical cables jutted up from the ground in tidy rows. Metal chillers coated the rooftops, and mazes of thick white piping covered the walls, pipes that will deliver closed-loop liquid cooling to data halls expected to come online in the months ahead.

That is the scale of one campus. There are hundreds more like it being scoped or built across the U.S. and globally.

“If you were to take every megawatt of power that data centers are bringing online, every megawatt needs a CDU connected to it, period,” Rich said. “That’s just the way it is. For every GPU or, let’s say, server that’s got several GPUs in it that’s going out, every single one of those needs to be connected to a CDU.”

A Father’s Legacy in Every Megawatt

Graham Whitmore did not live to see any of this. He passed away in 2015, a decade before Schneider Electric’s $850 million purchase, before Jensen Huang would call the deal a match made in heaven, and before his son would lead a flock of journalists through the company he built from nothing.

Toward the end of our conversation, Rich came back to his father one more time. He sat with the line for a moment before he said it.

“That’s like a true American success story that probably should be talked about more.”

I thought about that line as I got on a plane home the next day. Graham arrived in Buffalo in 1977 with Sylvia at his side, no relatives waiting at the gate, and a factory to open. Today, a large majority of the gigawatts of AI infrastructure being stood up around the world move through a coolant distribution unit that traces back, in an unbroken line, to that arrival.

Just two years ago, most companies were simply asking what AI could do in an enterprise setting. In 2026, they are asking a harder question: how to scale without breaking their reliability or their budget. That shift from curiosity to capacity is where Isayah Young-Burke, go-to-market strategist at IONOS, spends most of his time.

In a recent TechArena Data Insights episode, I sat down with Isayah and Solidigm’s Jeniece Wnorowski to explore why security and access risks are the underexamined obstacle in enterprise AI, how data sovereignty is reshaping infrastructure decisions on both sides of the Atlantic, and why storage is now one of the most strategic layers in an AI-ready stack.

An Expansive Vantage Point Across the Stack

IONOS, part of the publicly traded IONOS Group with more than 6.6 million customer contracts globally, occupies a distinctive position in the cloud market. The company serves customers ranging from an individual registering their first domain to an enterprise running a multi-client managed service provider business. That breadth, Isayah explained, provides a kind of ground-level intelligence that shapes how the company serves customers and thinks about AI adoption.

“It's that customer service and that experience we carry behind our brand. It has to be good at every level,” he said. “AI adoption…doesn’t just start with AI. It starts with that digital footprint that grows into infrastructure. AI becomes that natural next step, just like after you get a website, you start thinking about cloud storage and cloud infrastructure. So we get to see that whole journey.”

The Real Gaps: Security, Data and Skills

When asked where he sees the biggest gaps as organizations operationalize AI, Isayah was direct: most enterprises are focused on the wrong thing. While model selection often dominates the discussion, choosing the “right” model is not what predicts success.

“Most AI challenges at scale — it’s not really a capability problem. It’s a system problem, not the model. And increasingly, they are a trust and access problem,” he said.

He drew on a panel discussion at IT Expo where a fellow speaker raised concerns about the level of access AI agents are granted within enterprise environments. An agent embedded in a company’s internal systems can do more than answer questions. It can write, delete and trigger workflows across an entire environment. “That’s a very different risk profile than a website chatbot,” Isayah noted.

Beyond security, he identified data readiness and workforce skill gaps as persistent obstacles. IONOS has responded by building tools like IONOS Momentum and the AI Model Hub, designed to make AI infrastructure accessible to small-to-medium businesses and public sector organizations that need practical solutions, not just raw compute.

Data Sovereignty and the Regulatory Divide

Operating across the US and Europe gives IONOS a useful vantage point on how regulatory environments shape AI infrastructure decisions. In Europe, regulations like GDPR and initiatives like Gaia-X have made data residency a front-line concern from day one. In the US, speed and innovation tend to dominate, but that is shifting.

Isayah pointed to a dimension of US cloud law that often goes unexamined: the Cloud Act gives the US government legal authority to access data held by American cloud providers, even when that data is stored in Europe. IONOS operates under a different legal framework in Europe, because it is a subsidiary of a German company. This distinction matters significantly to companies that do business overseas.

“Knowing where your data lives and who has access to it under what conditions really matters,” he said. “Providers who can give answers to those questions have a real advantage.”

Storage as Strategic Infrastructure

Nowhere is the infrastructure shift more visible than in storage. Isayah described storage as having “quietly become one of the most strategic layers in AI,” noting that as AI-enabled workloads scale, enterprises must manage massive volumes of unstructured data, including text, images, logs and embeddings, that traditional storage architectures were never designed to handle.

With this new challenge, he noted, there’s been a shift toward object storage. The medallion architecture approach, organizing data into bronze, silver and gold enrichment tiers, has become a common framework for managing this complexity. These practices have become the backbone for data lakes, the central repositories of where raw data lives before being processed. S3-compatible object storage has emerged as the de facto standard for these data lakes, valued for its scalability, cost efficiency and — through IONOS — API accessibility.

Preparing for Agentic AI

Looking ahead, Isayah sees agentic AI as the next major infrastructure challenge. “AI agents aren’t just generating outputs,” he said. “They’re interacting with back-end systems. They’re triggering workflows from different applications and different software, making decisions across platforms in real time.”

That shift demands decentralized architecture, low-latency edge and cloud environments, strong API interoperability and, above all, rigorous security controls. He referenced Anthropic’s recent decisions around its Mythos model, where it chose not to release the model publicly after it was tested offensively in a sandbox experiment, found system vulnerabilities and escaped the test environment as instructed, as a reminder of what is at stake.

“Without fail-safes, it’s unwise to release this into the public,” Isayah said. “The foundation for these automated systems has to be solid.”

The TechArena Take

For technology decision-makers, the practical takeaway from this conversation is straightforward: the infrastructure decisions being made now around storage architecture, data governance and agent access controls will determine the ability of organizations to scale AI later. IONOS’s position gives Isayah a grounded view of where those decisions are going well and where they are not. Organizations still treating storage as a commodity and AI security as an afterthought, may find that catching up later is considerably more expensive than getting it right now.

To learn more, listen to our full conversation in the published podcast, and read about IONOS’s cloud offerings, including the AI Model Hub, at ionos.com.

Our next TechArena Data Insights comes live from Xcelerated Compute, where Jeniece Wnorowski and I had a chance to sit down with David Moehring, general partner at Cambium Capital and former CEO of IonQ, to unpack how quantum computing fits into the broader computing landscape, and why the real story is less about breakthrough moments and more about building entire ecosystems.

From Physics Labs to VC

David has had a long and illustrious career working in quantum across academia, government and industry. His career began in academia, where he pursued a PhD in atomic and optical physics and quantum computing before moving into applied research at Sandia National Labs. From there, he transitioned into government, funding advanced quantum computing initiatives, and became the founding CEO of IonQ before his move to Cambium Capital.

That multidisciplinary background continues to shape how he evaluates companies today. “I found it very useful to understand the motivations of all of these different parts of the ecosystem,” he said, adding that it greatly informed how he looks at investing into quantum.  

Systems-Level Thinking  

Cambium Capital is an early-stage venture capital fund that invests in advanced computer hardware, and we talked about their recent moves in investing in the total cost of ownership (TCO) of AI data centres.

Rather than chasing a single breakthrough, the firm invests across the compute ecosystem, focusing on improvements to be made in different areas. “We have companies that we’re looking at as part of our portfolio that work in power delivery, movement of data, embedded memory, and packaging across the ecosystem,” he explained, “because if you move just one of them forward, everything else is still backed up.”

This systems-level thinking extends to how Cambium evaluates startups. Technical depth is essential, but so is market readiness. “You just can’t have your one good technology and toss it over the fence and think other people are going to integrate it,” he notes. “You need to be very deeply technical in your field, but you also need to really understand where it comes in.”

Complement, Not Replace

When it comes to quantum computing, David is clear-eyed about both its promise and its limitations. The technology is not poised to replace classical systems; it will augment them. “It’s not that if you make quantum computers then classical computers or GPUs will go away because they really solve very different problems,” he said.

Instead, quantum systems will tackle specific classes of problems that are either infeasible or inefficient for classical architectures. This mirrors the evolution of GPUs and specialized accelerators, each designed for distinct workloads.

“There are jobs that just cannot be done by classical computing,” David explained, framing quantum as another layer in an increasingly heterogeneous compute landscape.

On the relationship between AI infrastructure and quantum computing, he envisioned both evolving in parallel, solving very different problems in the long run. That being said, he noted that classical computers will still be required to control quantum computers.  Beyond that, foundational innovations in materials science and lasers are likely to benefit both domains.

Building the Quantum Ecosystem

Cambium’s investment strategy isn’t limited to headline-grabbing processors. The firm also has strong conviction in the new quantum investment vehicle, 55 North, where David is the Board Chairman.  

“There’s still a lot of hardware development that is needed, not just for the kind of the quantum processor itself, but the ecosystem,” David said.

That includes everything from laser systems for atomic qubits to cryogenic infrastructure for superconducting systems, components that rarely make headlines but are essential to scaling the technology. This mirrors Cambium’s broader philosophy: meaningful progress happens when the entire stack evolves together.

Misconceptions and Early Signs  

Despite growing interest, quantum computing remains widely misunderstood. Even quantum physics itself can be very counterintuitive, and many physicists struggle with some of the rules. That gap between theory and practical understanding often fuels unrealistic expectations. His best advice? Talk to the experts.

Looking ahead, David points to a specific category where quantum could first demonstrate real-world value, the field of biopharmaceuticals. “We strongly believe that’s where you’re going to see first real kind of advantage from quantum,” he predicted.

At the same time, he remained cautious about overblown claims, adding that some of the news was driven more by hype than understanding.

The Tech Arena Take  

David built on his years of experience working on quantum in different capacities to deliver a pragmatic take on where the field is headed. Our key takeaway was that along with the big-ticket processors, investments need to focus on building the underlying infrastructure needed to keep quantum compute running, something Cambium Capital is keenly focused on. Pushing one piece of the puzzle without solving other challenges would only lead to bottlenecks being moved down the line.  

Watch the interview here.

TechArena won two Awards of Distinction at the 32nd Annual Communicator Awards, one of the largest and most competitive international programs honoring excellence in marketing, communications, and creative work. The awards were announced May 5, 2026, by the Academy of Interactive & Visual Arts (AIVA).

The recognized work spans two categories in the Writing, Business-to-Business division:

The TechArena Forum earned a Distinction award in the Series category, recognizing the platform's ongoing editorial commitment to hosting rigorous, expert-led discussions across AI, data center, cloud, edge, networking, and sustainability.

Rachel Horton’s report titled, “Open to Work: The Quickening of AI & the Future of Jobs” earned a Distinction award in the General category for its unflinching examination of how artificial intelligence is reshaping employment faster than workers, companies, and policymakers can keep pace.

Forum: Where Expert Voices Meet

The TechArena Forum is a media platform designed to bring together domain experts, frontline technologists, and senior executives for substantive interviews and analyses about the technologies shaping data center and AI infrastructure. Topics range from AI model efficiency and data center energy consumption to network architecture and quantum computing readiness.

What sets Forum apart is its editorial standard. Every contribution goes through a process built on decades of journalism and communications experience, ensuring that the insights published carry real weight with technical and business audiences alike. TechArena staffers recognized for this award include CEO & Founder Allyson Klein, Head of Content & Editorial Rachel Horton, Writer/ Editor Deanna Oothoudt, Editorial Manager Francisco Sipiora Gutierrez, and Graphic Designer/ Videographer Kirk Hansen. The Communicator Awards jury, which this year included professionals from JPMorgan Chase & Co., FedEx, Netflix, National Geographic Society, Accenture Song, and the NAACP, recognized that standard with its Distinction honor.

A Report That Struck a Nerve

The “Open to Work” report arrived at a moment when the AI jobs conversation had reached a tipping point. Published in August 2025, the report traced the human cost of rapid automation, from Intel’s sweeping layoffs to the quieter, less visible displacement happening inside enterprises that had begun replacing roles without public announcements. It explored the widening gap between corporate productivity gains and the stress, uncertainty, and reskilling pressure bearing down on individual workers.

The piece resonated with readers because it refused to pick an easy side. It acknowledged the genuine economic value AI creates while documenting the uneven distribution of that value and the institutional failures slowing workforce adaptation. That combination of analytical rigor and human-centered storytelling is the kind of work TechArena was built to produce.

Building on Momentum

These two wins mark TechArena’s latest international recognition. Earlier this year, CEO & Founder Allyson Klein was named 2026 Female Founder of the Year by the Global Business Tech Awards. In 2025, the company’s flagship podcast series, “In the Arena,” earned a Stevie Award in the Shows, Technology category at the International Business Awards, praised for its production quality, editorial clarity, and executive-level guest caliber.

The pattern is no accident. Since its founding, TechArena has assembled a team with more than 100 years of collective experience across the technology sector and has welcomed executives and innovators from companies representing more than $9 trillion in market capitalization, including Microsoft, NVIDIA, Google, and Meta. That depth of access and expertise shows up in content that consistently earns trust from both the industry it covers and the awards programs that evaluate creative and communications work on a global stage.

“We set out to build a media platform where deep technical knowledge meets editorial craft,” Klein said. “These awards validate that mission and the talented team behind it.”

About the Communicator Awards

The Communicator Awards, now in its 32nd year, recognizes excellence, effectiveness, and innovation across all areas of communication. The program is sanctioned and reviewed by the AIVA, an invitation-only body of more than 1,100 industry leaders from top brands and agencies. This year's competition drew thousands of entries from organizations across the United States and around the world.

For more information, visit communicatorawards.com.

About TechArena

TechArena brings together business advisory, world-class marketing with tech domain expertise, and trusted insights for companies shaping the AI era. The platform covers AI, data centers, semiconductors, cloud-native infrastructure, networking, edge computing, and sustainability.

Something changed quietly in 2024. The people trying to break into your accounts stopped being people. They handed the job to machines specifically, to AI models trained on billions of leaked credentials, capable of generating contextually plausible password guesses faster than any human could conceive.  

Today is World Password Day, and the tired advice about mixing uppercase letters and numbers is not just insufficient. It may be actively misleading you about the scale of the problem.

The numbers are unambiguous. 2.86 billion credentials were stolen in 2025 alone, and credential-based attacks now account for roughly 22% of all data breaches, making stolen logins the single most common initial attack vector in cybersecurity today. The average cost of a breach reached $4.88 million last year. But the cost you hear about least is the one borne by ordinary people whose bank accounts, email inboxes, and digital lives are quietly taken over.

AI Has Changed the Attack, Not Just the Speed

The brute-force attack of a bored hacker typing guesses one by one is a relic. Modern AI-assisted password attacks work by training models on enormous breach datasets and generating statistically likely variations of your real password. If you use Liverpool#1 on one site, the model predicts you might use Liverpool1! or Liv3rpool# on another. It does not guess randomly. It reasons.

Credential stuffing automated login attempts using leaked username and password pairs from old breaches has been AI-supercharged. Bots now attempt logins roughly every 39 seconds across thousands of services simultaneously, adapting in real time to rate-limiting defenses. A parallel and newer threat has emerged alongside it: prompt injection, where attackers embed malicious instructions inside documents, emails, or data that an AI assistant will read, causing it to act against the user's interests without any visible sign of compromise.

In June 2025, researchers disclosed EchoLeak (CVE-2025-32711), a zero-click vulnerability in Microsoft 365 Copilot that allowed a remote attacker to steal confidential files simply by sending a crafted email with no user interaction required. Wiz Research tracked a 340% year-over-year increase in documented prompt injection attempts against enterprise AI systems in Q4 2025 alone. The threat surface has expanded beyond your password to every AI system that holds your identity and data.

Phishing has also been transformed. Where once a phishing email was recognizable by its awkward grammar and implausible urgency, generative AI now produces personalized lures that pass for genuine correspondence from your employer, your bank, or your healthcare provider. MFA fatigue attacks where an attacker triggers repeated push notification prompts until an exhausted user simply approves one, rose 217% year-over-year according to the 2025 Verizon Data Breach Investigations Report. These are not future threats. They are operating at scale right now.

The Threat That Does Not Need Your Password at All

Perhaps the most unsettling development is the rise of attacks that bypass the password entirely. Voice cloning AI can now synthesize a convincing replica of a person's voice from as little as three to ten seconds of publicly available audio, a LinkedIn video, a podcast appearance, a conference recording. In April 2025, security journalist Joseph Cox demonstrated this by using a $20 AI voice tool to clone his own voice and successfully pass one of the Bank's voice authentication system, gaining full account access. That was a controlled test. Real attackers using the same tools against unwitting victims have achieved identical results.

Voice cloning fraud increased 400% in 2025. Deepfake video is now available as a service no technical expertise required. Criminals used AI voice cloning to steal $35 million from a bank in the UAE and $243,000 from a UK energy company whose finance director received a call that sounded exactly like his CEO. The implication is uncomfortable: protecting your password is necessary, but no longer sufficient. Your voice, your face, and your session cookies are now potential attack vectors too.

What to Actually Do - In Order of Impact

Use a password manager and stop inventing passwords. Roughly 94% of passwords used today are either weak or reused. The single highest-leverage action you can take is delegating password creation entirely to a password manager. Let it generate 20-character random strings. You no longer need to remember them only the master password matters.

Replace SMS two-factor with an authenticator app or hardware key. SMS codes are vulnerable to SIM-swap attacks, where a criminal convinces your mobile carrier to transfer your number to their device. Authenticator apps like Google Authenticator, Authy, Microsoft Authenticator are significantly more resistant. A hardware security key such as a YubiKey is the strongest option for your most critical accounts.

Enable passkeys wherever available. Passkeys are cryptographic credentials that replace passwords entirely. Because they are mathematically bound to the specific website that created them, they cannot be phished a fake login page cannot harvest a passkey. Eight of the world's ten most-visited websites now support passkeys, and over a billion have been created globally. Setting one up on your Google, Apple, or Microsoft account takes under two minutes and eliminates an entire class of attack.

Check haveibeenpwned.com today. This free service tells you which of your email addresses have appeared in known data breaches. If yours has, that password and any others you may have reused with it should be considered compromised and changed immediately.

Be skeptical of voice calls requesting account access. Given the demonstrated capability of voice cloning tools, any unexpected call claiming to be from your bank, employer, or a technology company should be treated with suspicion. Hang up and call the institution directly on a verified number. This is no longer paranoia it is standard practice.

The Bigger Picture

Passwords are not going away in 2026. Most services still require them, and the transition to passkeys is expected to continue well into 2027. But the frame has shifted. The question is no longer how to choose a better password; it is how to reduce your dependence on static secrets altogether.

AI has industrialized credential theft. The response must be equally systematic: structured use of a password manager, elimination of SMS-based verification, adoption of passkeys where available, and a new baseline skepticism about any communication that asks you to confirm who you are. The attackers' tools do not sleep, do not get distracted, and do not take days off.

Fortunately, neither do the defenses, if you put them in place.

World Password Day is observed annually on the first Thursday of May.

365 Data Centers and Aphorio Carter, the critical infrastructure and data center division of Carter Funds, announced a strategic partnership on May 6 to develop approximately 200 megawatts of AI-ready data center capacity across six U.S. sites. The first letters of intent target Aurora, Colorado, and Simpsonville, Kentucky, with additional locations planned in Trumbull, Connecticut; Louisville, Kentucky; Harrisonburg, Virginia; and Columbus, Ohio.

The details matter here. These are not greenfield projects. 365 and Aphorio Carter plan to identify, convert, and develop existing properties into high-density facilities supporting 50 to over 200 kilowatts per cabinet, using liquid-to-chip cooling designed for AI and high-performance computing workloads. 365 will serve as long-term operator across the portfolio, with projects expected to come online within nine to 24 months.

“Through this partnership, we’re in an ideal position to create a new class of high-density infrastructure designed specifically for AI-era workloads,” said Derek Gillespie, CEO and CRO of 365 Data Centers. “Working with Aphorio Carter will allow us to create new value in existing assets while bringing new capacity online to support today’s demand.”

Nine to 24 months. In a market where greenfield data center projects now take three to five years from announcement to first megawatt, that timeline is the real story. And the conditions creating demand for this kind of speed are only intensifying.

A $725 Billion Spending Spree Collides with a 15 GW Grid

The capital flowing into AI infrastructure has reached a scale that’s difficult to contextualize. Google, Amazon, Microsoft, and Meta collectively plan to spend $725 billion on capex in 2026, a 77 percent increase over last year’s record $410 billion.

Fortune reported in late April that the buildout shows “no clear end in sight.” The Stargate Project, a government-backed initiative led by OpenAI, Microsoft, Oracle, and SoftBank, has committed $500 billion toward AI-first data centers by 2029.

The money is not the constraint. Power is.

The entire U.S. data center sector draws less than 15 gigawatts of electricity today. The announced project pipeline, if every facility were built, would pile 140 gigawatts of new load onto that grid. That’s roughly nine times the current draw. And the grid is nowhere near ready to absorb it.

Of 12 GW of data center capacity announced for 2026 delivery, only about 5 GW is under active construction. Roughly half of all planned 2026 builds have been delayed or canceled outright. The reasons are physical, not financial. Lead times for high-voltage transformers have ballooned from 12 to 18 months before 2020 to 36 to 48 months today, with some orders stretching to five years. Grid interconnection wait times exceed five years in many U.S. regions. Beyond what’s currently under construction, an additional 37 GW of planned infrastructure lacks firm completion dates, pushing the cumulative pipeline gap past 50 GW of announced-but-unbuilt capacity.

America’s investor-owned utilities have responded with $1.4 trillion in capital spending plans through 2030, up 27 percent from the prior year’s projection. But grid upgrades move on timelines measured in years, not quarters. The gap between committed capital and deliverable megawatts continues to widen.

This mismatch has created an opening for operators willing to think differently about how AI-ready capacity gets built.

The Case for Building Inside Existing Walls

The brownfield conversion model attacks the binding constraint directly. Existing properties with committed utility power can bypass the two longest delays in data center development: interconnection queues and greenfield construction timelines.

The approach is gaining traction faster than most industry observers expected. Goldman Sachs models assume brownfield accounts for 15 percent of required data center space in 2026, growing to 30 percent by 2031. More than 70 percent of global data center capacity already resides in existing buildings, according to Schneider Electric. And brownfield retrofits can cut facility capital expenditure by up to 30 percent compared to greenfield builds, per Data Center Dynamics.

The most dramatic proof of concept arrived in 2024, when xAI assembled Colossus, its 100,000-GPU supercomputer, inside a shuttered Electrolux factory in Memphis in just 122 days. That project moved from empty industrial space to operational AI training cluster faster than most greenfield developers can secure permits.

Three forces have converged to make this model viable at scale.

The first is the power bottleneck itself. When interconnection queues run five years deep, any operator who can deliver capacity on existing grid connections holds a structural advantage. Speed-to-megawatts has become a premium asset in its own right.

The second is liquid cooling. Two years ago, retrofitting an existing building for AI-grade power density was impractical. Air cooling hit its thermal ceiling around 30 to 40 kW per rack. Direct-to-chip liquid cooling has upended that calculus. Goldman Sachs projects the share of liquid-cooled AI servers will climb from 15 percent in 2024 to 76 percent in 2026. Schneider Electric published a technical framework in February 2026 focused on brownfield liquid cooling retrofits, highlighting localized, staged upgrades as a strong fit for colocation and service provider environments. Design loads exceeding 100 to 200 kW per rack are now standard in new AI builds, and the AI data center liquid cooling market is projected to expand from $6.6 billion in 2025 to $61.8 billion by 2034.

The third is the shift from training to inference. Centralized mega-campuses make sense for massive training clusters. But inference workloads, the production side of AI where models serve real-time predictions and outputs, benefit from geographic distribution. Smaller facilities deployed closer to end users reduce latency and spread the load across multiple grid connections. The conversion model maps well to this emerging architecture.

Not every building qualifies, of course. Viable candidates need structural load capacity for high-density racks, proximity to utility substations with available or committed capacity, adequate water supply for cooling loops, and fiber connectivity. The differentiator is finding properties where utility power is already allocated or can be secured without entering the back of a multi-year interconnection queue. That’s where real estate expertise becomes just as valuable as engineering capability.

How the 365/Aphorio Carter Partnership Works

The 365 Data Centers and Aphorio Carter partnership pairs two distinct competencies. Aphorio Carter brings real estate investment, development, and asset management experience, backed by a leadership team that has collectively invested in and managed over $6 billion in data center real estate. 365 brings operational depth: colocation, connectivity, managed cloud services, and a pipeline of enterprise customers who need high-density capacity.

The division of labor is clean. Aphorio Carter sources and develops the properties. 365 operates them long-term. Together, they can move on multiple sites simultaneously rather than sequencing projects one at a time.

“We’ve aligned the delivery of utility power with critical infrastructure allowing us to provide scalable, high-density infrastructure where it’s needed most,” said John Regan, President and COO at Aphorio Carter. “This is a great partnership where we’ve got the real estate and the ability to supply the data center infrastructure inline with available utility capacity while 365 has a highly reliable O&M track record along with a healthy pipeline of customers. Together, we’re creating a scalable supply of power-rich environments that can be delivered faster and perform at a higher level than traditional developments.”

The geographic strategy reveals a deliberate calculation. Aurora, Simpsonville, Trumbull, Louisville, Harrisonburg, Columbus. These are not Northern Virginia or Dallas or Phoenix, the traditional hyperscale corridors where grid capacity is most constrained and competition for power is fiercest. These are secondary markets where utility power remains accessible, where interconnection timelines are shorter because operators aren’t competing with multi-gigawatt campus developments for grid access.

The nine-to-24-month delivery window stands in sharp contrast to the three-to-five-year timelines that are now common for greenfield projects, particularly in regions where transformer shortages and grid congestion have slowed permitting and construction.

The Window Is Open, and the Clock Is Running

Brownfield conversions will not replace hyperscale campuses. Meta’s multi-gigawatt Hyperion project in Louisiana, the Stargate consortium’s 7 GW expansion across Ohio and Pennsylvania, Microsoft’s $7 billion Fairwater campus in Wisconsin: these projects exist at a scale and serve a purpose that conversions cannot replicate. The industry’s largest training clusters will continue to demand purpose-built facilities with dedicated power plants and custom electrical infrastructure.

But training clusters are only part of the picture. For mid-market operators serving enterprise AI deployments, for the growing wave of inference workloads that demand geographic distribution, and for any organization that needs AI-ready compute capacity in the next 12 to 18 months rather than the next three to five years, the conversion model fills a gap that greenfield development physically cannot address on the timelines the market demands.

The brownfield model carries a sustainability advantage, too. Reusing existing structures means a lower embodied carbon footprint than new construction, a factor that enterprise procurement teams are weighing more heavily in vendor selection as corporate climate commitments run headlong into the energy intensity of AI.

The competitive field for brownfield AI infrastructure is still forming. Few operators have assembled a repeatable, scalable conversion playbook. The 365 and Aphorio Carter partnership represents one early template, but the opportunity set extends well beyond six sites in Colorado and Kentucky. The window exists because the power wall is not temporary. Transformer supply chains, interconnection backlogs, and permitting processes will take years to normalize even with the $1.4 trillion in utility investment now committed.

The AI infrastructure race will not be decided solely by who writes the biggest check. In a market where half of planned builds stall before breaking ground, the ability to deliver megawatts fast, inside existing walls, on existing grid connections, in markets the hyperscalers haven’t yet saturated, may prove just as consequential as any $10 billion campus announcement.

Maya Kalyan’s career has always lived at the intersection of disciplines. With a background in biomedical engineering and more than a decade in life sciences, she now serves as a staff algorithms and AI engineer in the molecular diagnostics space at Thermo Fisher Scientific. In a recent TechArena Data Insights episode, Solidigm’s Jeniece Wnorowski and I heard Maya offer a practitioner’s view on where AI is genuinely delivering value in healthcare, and where significant work remains.

Four Problems, One Goal

The starting point for any meaningful discussion of innovation in diagnostics, Maya explained, is understanding what problems the industry is actually trying to solve. She identified four primary areas of focus: accuracy and reliability, turnaround time, cost reduction, and automation.

On both cost and turnaround time, she pointed to how the increasing demand for molecular testing is being met by innovations like multiplexing, which is the ability to detect multiple pathogens within a single sample. “We have respiratory virus tests that can detect COVID and flu and RSV viruses all within the same test,” she said. “That reduces the reagent use and lowers your consumable cost while also increasing throughput.” The broader goal, she noted, is building diagnostic systems that are simultaneously faster, more reliable, more affordable, and capable of handling the volume demands of high-throughput clinical and research environments.

Where AI Works, and Where It Doesn’t

Maya offered a measured perspective on AI’s current capabilities, drawing a clear line between where the technology performs well and where it has room for growth.

AI tends to be most effective, she said, when working with large, well-structured datasets toward a defined predictive outcome, such as pattern recognition in biological data, quality monitoring in experimental workflows, and domain-specific assistants that help researchers navigate documentation or troubleshoot instruments. The benefit, she noted, is improving the user experience and reducing manual touchpoints.

The limitations, however, are equally important for technology decision makers to understand. “When it comes to large language models, specifically the risk of hallucinations and its non-deterministic nature — where it can make up things or not say the same thing each time — can be a barrier to adoption in scientific or healthcare settings.” Her prescription is a hybrid approach: one that keeps human expertise in the loop by design, even as agentic AI systems grow more capable of autonomous workflows.

Regulatory Reality

Building AI-enabled diagnostic products is not simply a technical challenge. Maya outlined a layered set of constraints that shape every deployment decision, starting with data governance. Healthcare datasets often contain sensitive patient or genomic information. Considerations for privacy affect how data can be accessed, shared, and used in ways that go well beyond standard HIPAA compliance.

There are also practical deployment decisions with regulatory implications: whether AI systems run in the cloud or directly on an instrument, and how factors like connectivity and latency influence what’s feasible. And once a model is deployed, the work isn’t over. “Teams need some kind of post-market surveillance plan,” she said, “which requires a strong model observability service where they can monitor the performance of the model and identify any drifts.” In practice, applying AI in this space means balancing innovation against a set of strenuous operational and regulatory realities.

The Data Foundation Problem

Before AI can meaningfully contribute to product development or diagnostics, Maya emphasized that organizations need to get their data house in order. That begins with rigorous data curation, ensuring experimental data is well-annotated and collected consistently so models can learn real patterns rather than artifacts of poor methodology.

Accessibility is the other piece. In many research organizations, data is scattered across instruments, labs, and databases with no unified infrastructure to bring it together. Maya pointed to large open biomedical datasets such as the Cancer Genome Atlas curated by the National Institutes of Health as important resources the research community already relies on. Looking ahead, she sees federated data approaches, which enable collaboration without requiring the sharing of raw patient data, as critical to accelerating AI’s role in diagnostics at scale.

The TechArena Take

While grounded in biomedical engineering, Maya’s perspective reflects broadly applicable lessons: the most durable AI deployments are built on disciplined data practices, realistic expectations, and a clear-eyed understanding of evolving regulatory requirements. In a field where the stakes are measured in patient outcomes, the pressure to get it right is acute. If AI lives up to its potential in life sciences, the payoff won’t just be operational efficiency. It will be earlier diagnoses, more personalized treatments, and meaningfully better quality of life for patients facing some of the most challenging medical conditions.

The enterprise AI landscape is littered with promising ideas that haven’t made it past the proof-of-concept stage. Companies are investing in AI capabilities, yet the path from controlled experiment to production-grade system remains one of the most persistent and costly bottlenecks in the industry. The gap demands more than technical talent. It requires the kind of operational and financial rigor that separates sustainable growth from expensive experimentation.

Following the recent launch of the TechArena Advisory, we are excited to highlight the exceptional operators bringing C-suite-grade strategic intelligence within reach of organizations at every stage of growth. The Advisory represents our commitment to providing a high-impact alternative to traditional consultants, offering the strategic blueprints of those who have already built and scaled multi-billion-dollar businesses.

Laura St. John has spent more than two decades in the trenches of finance, strategy, and operations, building the kind of pattern recognition that only comes from roles where the numbers had to hold up and the decisions carried real consequences. As co-founder of MisaLabs, she is now channeling that experience into solving the pilot-to-production problem head on, helping enterprises build AI systems designed to scale from day one. In this edition of our “5 Fast Facts” Q&A series, she discusses why speed without clarity is the biggest risk in enterprise AI today, what separates pilots that scale from those that stall, and how disciplined decision-making remains the most underrated competitive advantage in a market moving at full velocity.

Q1: What has changed in the tech landscape that made the Advisory a priority for you?

This shift is personal for me. After years inside large organizations, I stepped into a startup environment and started seeing how quickly the ground is moving for business leaders.

Everyone knows AI is changing the pace. That part isn’t news. What caught my attention was a pattern I kept running into: companies pouring energy into AI proof of concepts that never made it past the demo stage. Lots of experimentation, very little making it into how the business actually runs day-to-day.

That gap between “interesting pilot” and “operating at scale” is what pulled me into advisory work. The technology conversations are happening everywhere. The harder conversation, the one about whether your organization is actually set up to absorb and scale what you’re building, isn’t happening nearly enough.

Q2: What does your experience bring to this moment?

More than two decades across finance, strategy, and operations, mostly in roles where the numbers had to hold up and the decisions carried real weight. I’ve been on both sides of it. Building the story, and getting challenged when the data didn’t reconcile. Those moments stick with you. You develop a sharp instinct for separating signal from noise.  One that stands out was an investment in a new TV technology. The whole team was excited, the technology was compelling, and the financial projections looked great. Then the CFO asked a pointed question about the ASP, the average selling price we were projecting for the new product, and gave us some homework: could we prove that consumers would actually pay that much of a premium? After some digging, we found the ASP we had baked into our model was more than 5x the price jump the market absorbed when TVs went from black-and-white to color. Even adjusted for inflation, we were well beyond anything the market had ever supported. The technology was real. The business case wasn't.

That pattern recognition is what I lean on most right now. There’s no shortage of data or dashboards in any organization today. The question that trips people up isn’t “what can we see?” It’s “do we trust what we’re looking at, do we understand what’s behind it and do we know where it came from?”

Q3: What challenges are business leaders facing that align with your practice areas?

Speed without clarity. That’s the simplest way to put it.

Leaders are under enormous pressure to adopt tools, automate decisions, and scale quickly. In many cases, though, the underlying data isn’t connected and operating models aren’t ready for that velocity. The result is that companies end up scaling decisions without fully trusting the inputs behind them. Dashboards can create a false sense of certainty.

The other pattern I see consistently: strong top-line growth masking what’s underneath. Revenue climbs, and it hides margin pressure, operational drag, cost structures that haven’t been questioned in years. Then growth slows, and all of it surfaces at once.

The work I focus on is reconnecting speed with fundamentals. Understand the business model. Trust the data. Know what’s actually driving performance before you pour fuel on it.

Q4: Are there key areas you see as most pertinent right now?

The intersection of AI adoption and financial discipline. That’s where the tension is highest.

I see companies land in one of two places. Some stall because competing priorities create indecision and gridlock. Others rush into investment without a clear picture of usage or value. This creates pressure to run to a proof of concept.  They’re an easy way to show progress. They’re also controlled, narrow, and disconnected from enterprise complexity.

Here’s the part that doesn’t get said enough: pilot success rarely translates into production success. The requirements are fundamentally different. Integration, security, reuse, and governance. Those constraints don’t show up in a pilot, but they’re the reason most AI efforts stall at scale.

That is what led us to start MisaLabs. Most AI systems fail to scale for the same reason: enterprise requirements like security, compliance, and infrastructure are treated as afterthoughts.  We built MisaLabs to reverse that, embedding those constraints into the foundation so teams can move from pilot to production without starting over.

Q5: What outcomes are you targeting to drive?

Fewer blind spots. Faster decisions that people can stand behind.

In practice, that means narrowing in on what drives the business, aligning teams around those priorities, and stripping out the noise that slows execution. Not more dashboards. Better understanding of what the dashboards are telling you.

It also means building confidence in the system. Leaders should know where their data comes from, what’s behind a trend, and when something doesn’t add up. That’s when better decisions happen.

The long game is durable, profitable growth. Not just momentum or activity. If I can help teams connect strategy to execution and sidestep a few of the pitfalls I’ve seen play out over the years, that’s a good outcome.

There’s a conversation happening at the edges of the AI infrastructure world that hasn’t quite broken through to the mainstream yet. It’s not about which GPU cluster wins the benchmark race or which hyperscaler is adding the most capacity. It centers on something far more fundamental: the cost of moving data.

In a recent Data Insights episode, I sat down with Solidigm’s Jeniece Wnorowski and Nilesh Shah, VP of Business Development at ZeroPoint Technologies, to work through where this friction in modern AI systems lives.  

Ten Times More Expensive to Move Than to Compute

Nilesh began with an often-overlooked aspect of data storage: the amount of power moving data takes. Moving a single bit of data from storage, through high-bandwidth memory or low-power double data rate (LPDDR), and into the on-chip static random access memory (SRAM) where computation actually happens costs roughly ten times more in power than performing the computation itself. That ratio explains why inference chip innovators like Groq, Cerebras and SambaNova are focusing on data movement and memory hierarchies over compute.

Zero Point Technologies was founded on the premise that the need for data and memory is going to increase rapidly, and one of the ways to tackle that challenge is through lossless memory compression. By reducing the volume of data physically moving across the system, you increase the effective bandwidth and capacity of the compute engine.

Agentic AI Compounds the Problem

On the question of whether AI workflows were being constructed correctly for the management of data, and how this could change as enterprises start scaling inference into different parts of their business, Nilesh pointed out that the key problem to be solved is agentic AI entering the workflow.

A pattern seen at recent tech conferences was that chip designers were integrating multiple specialized AI agents into a single electronic design automation (EDA) workflow, each handling a distinct task, like error detection or chip verification. This would mean having domain-specific inference solutions for even EDA operations, fundamentally changing the way enterprises will need to think about data.  

Prefill vs Decode Stage

As data becomes a challenge, memory bandwidth could become a bottleneck. Nilesh pointed out that agentic workflows and inference takes place in two stages, prefill and decode. The prefill stage processes the input prompt and is genuinely compute intensive. Modern GPU clusters handle this part reasonably well. The decode stage, where the output is generated, is extremely memory intensive and is what’s really limiting tokens per second.

When it comes to responsiveness at enterprise scale, say 100,000 employees simultaneously interacting at that scale across multiple streams of data, the decode phase becomes a real bottleneck. At NVIDIA GTC 2026, a lot of the keynotes revolved around developing heterogenous architectures that can manage the decode phase more efficiently.

We talked about when quantum computing would enter the picture. “What is the ChatGPT moment for quantum computing? That’s the favourite question I like to ask,” said Nilesh. He predicted that it could make sense to attach quantum processing units to data centers to efficiently offload some of the compute work that quantum tends to do well. There are currently examples of banks deploying early quantum computers, and another use case could be encryption and creating more secure encryption protocols.  

Future Innovation in Memory  

When I asked Nilesh what he sees on the horizon for memory and storage technology, he outlined three distinct directions where investment and innovation are converging.  

The first is alternative memory technologies. Dynamic random-access memory (DRAM) is a decades-old architecture that hasn’t changed fundamentally, and its limitations are starting to bite at exactly the moment AI workloads are scaling fastest. The second is new interfaces between memory and compute that will transform how memory communicates with the compute engine.

The third is the most significant shift in perspective: the unit of infrastructure design is moving from the chip, to the server, to the rack, and now to the data center as a single coherent system. Organizations are thinking about AI infrastructure in terms of megawatts allocated to a data center, with memory, storage, and compute all traded off within that power budget.

The biggest misconception, he felt, was the assumption that scaling AI output will keep being built on a proportional increase in power. “I expect a breakthrough that someone will come up with an entirely new style of physics that will break that linear assumption that to go from 100 LLMs to a million or going from a million users to 100 million, we’ll just multiple the megawatts of power,” he said.  

The TechArena Take

My conversation with Nilesh clarified a change in direction I’ve noticed at many recent tech conferences. The 10x cost differential between moving data and computing on it is the reason the entire inference chip landscape looks the way it does. It’s a significant engineering constraint that companies like ZeroPoint are building directly against. The prefill-decode distinction matters because enterprises planning inference deployments at scale need to architect around the decode phase as a distinct bottleneck.

We’re excited to see what new innovations take place in the memory space, and if, as Nilesh believes, someone will eventually find a way to scale AI without the linear progression of more compute meaning more power.  

Watch the podcast here.

Quantum computing conversations tend to get pulled toward the exotic: superposition, quantum entanglement, and what a world with exponentially faster compute will look like. But at the Xcelerated Computing Conference in New York, Solidigm’s Jeniece Wnorowski and I spoke with Burns Healey, quantum infrastructure lead for Dell, who offered a grounded perspective. For quantum technology to matter, it first needs a place to land, and that place is working alongside the classical data center.

It’s a framing that shapes everything about how Dell is approaching the market and one with practical implications for technology decision makers weighing when and how quantum fits into their roadmap.

A Hybrid Future

Our conversation started with reconsidering the terms we use when we talk about quantum technology. “Quantum computers are almost a bit of a misnomer,” Burns said. “When you say quantum computer, I prefer to use the term quantum accelerator, because really that’s what they are. They’re an add-on to HPC or data center infrastructure that give you specialized options for computing specific workloads.”

This perspective that quantum technology is best considered as an extension of high-performance computing environments can be helpful to enterprise leaders who may feel pressure to engage with quantum. Organizations attempting to adopt quantum before they’ve pushed classical computing to its limits are, in his view, getting ahead of themselves.

“Going to a quantum computer before you’ve attempted to use classical HPC or large data center environments is a bit like trying to run before you’ve walked,” he said. “Only once you hit those limits in your data center, in your HPC environment, will you start to think about what quantum can do that you can’t currently do.”

Lowering the Barrier with Logical Qubits

Much of the early quantum conversation focused on physical qubit (quantum bit) counts and error rates, but recent conversations in quantum computing have shifted toward logical qubits and error correction as the field considers what usable quantum will look like.

Burns drew a direct analogy to classical computing. Just as error-correcting code allows applications to run more reliably, logical qubits aim to provide a stable, abstracted layer above the physical qubit substrate.

“The way we use them from a vendor and hardware supplier viewpoint is that we are going to aim to abstract away a lot of that physical layer complexity from the end user,” he said. “It’s a lowering the barrier to entry question in my mind, and the best way we can help onboard new people to the technology.”

Closing the Latency Gap With NVIDIA

When you think of quantum computers as quantum accelerators, the importance of the infrastructure that enables quantum and classical computing to work seamlessly becomes paramount. Rather than building quantum processing units (QPUs), Dell is helping produce the ecosystem and infrastructure appliances that will make quantum devices usable within real data center environments. A major challenge in that area is latency between quantum and classical systems. Burns pointed to Dell’s collaboration with NVIDIA as a current example of this work.

NVIDIA has developed a framework called NVQLink, designed to minimize the round-trip latency between QPUs and classical compute. Using NVQLink on Dell PowerEdge servers, the two companies recently demonstrated sub-4-microsecond latency, a result Burns described as meaningful progress toward the kind of tight integration that real quantum workloads will require.

“We’re really looking at what the technology needs in terms of specifications and hitting those targets to make this infrastructure usable for real quantum computing,” he said.

Dell is also engaged with quantum partners including QuEra and IQM, as well as a joint research initiative with Ernst & Young, all documented on Dell’s hybrid quantum-classical computing page.

The Path to Scale

When asked what needs to happen technically and operationally for quantum to move from research settings to deployable infrastructure, Burns identified two parallel tracks.

On the software side, progress is already underway. Frameworks like IBM’s open-source Qiskit are helping developers work with quantum gates and algorithms today. The next meaningful shift will come when developers can work at a Python-level abstraction, or eventually through application-specific tools that require no quantum expertise at all.

On the hardware side, cabling is one of the more pressing unsolved problems. Superconducting qubit systems require analog signals routed to each individual qubit. At 50 or 100 qubits, that is manageable. At thousands or millions of qubits, the cabling architecture becomes an issue. Ideas to address this include embedding classical components inside dilution refrigerators and more sophisticated multiplexing approaches, both of which introduce their own challenges.

The TechArena Take

Dell’s positioning in the quantum space is as perceptive as you would expect from one of the world’s classical computing giants. Rather than competing with QPU vendors, the company is focused on the infrastructure layer that will make quantum systems usable in real enterprise environments.

Burns’s framing of quantum as an accelerator, not a computer, is a useful corrective for organizations trying to calibrate their engagement with the technology. For most enterprises, the near-term question is not whether to adopt quantum, but how to ensure that classical infrastructure is ready when quantum workloads become viable. The organizations with the strongest HPC foundations will be best positioned to take advantage of it.

Listen to our conversation in the full podcast episode, and for more information about Dell’s hybrid quantum-classical computing work is available on Dell’s quantum computing site.