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Quantum technology has spent years as a domain defined by its promise rather than its deployments. That is beginning to change. The question now is not if quantum belongs on technological roadmaps for real business environments but how quickly it can be made reliable, integrated and usable.

That challenge sits at the center of the work being done at Q-CTRL. Recently, I had the chance to explore that work with the company's vice president of product, Alex Shih, alongside my "Data Insights" cohost, Solidigm's Jeniece Wnorowski. What emerged from the conversation is a clear view that the path for quantum, from laboratory demonstration to enterprise deployment, runs directly through software.

From Deep Science to Deployable Solutions

Q-CTRL's core work is translating quantum physics and quantum control expertise into products that customers can run. Alex described his role as taking deep science and turning it into scalable solutions for data centers, high-performance computing facilities and enterprise businesses. The company also delivers a quantum education platform to help organizations build and scale quantum talent, along with quantum sensing solutions for navigation.

Alex came to the field after a career spanning big technology companies, startups and space technology, most recently at Slack. He was drawn to quantum for its long-term potential. "I've always viewed quantum as a generational step-change technology that would introduce a new compute paradigm," he said.

Controlling Instability With Software

The central technical challenges in quantum systems are hardware instability, driven in large part by noise from both the hardware itself and the surrounding environment, and the difficulty of scaling that hardware. Q-CTRL develops what Alex described as AI-powered error-reduction quantum infrastructure software, which reduces that noise and suppresses the resulting errors so the hardware behaves reliably enough to build on.

That reliability serves two audiences. For hardware teams, it helps accelerate the development of their qubits. For enterprises developing and testing applications on real quantum hardware, it means the output can be trusted, which, as Alex noted, is what allows customers to properly assess how their applications perform.

Built for the Real World, Not the Journal

Alex was emphatic that Q-CTRL's purpose is to put quantum tools into working business environments. "While important to showcase progress in the field, we are not in the business of just publishing papers but rather removing barriers for customers to use our tools," he said. The goal, he explained, is for those tools to be accessible and used by companies around the world.

That focus is reflected in a recent deployment. Q-CTRL went live in Colorado with Elevate Quantum in a time frame that Alex described as a record for the industry, moving from the announcement of a planned system to a working one in only five months, at a fraction of the typical cost. He framed the achievement as a blueprint rather than a one-time result, expecting subsequent deployments to move even faster. "We've seen Murphy's Law at play; we've seen everything that can go wrong, did go wrong. But now we have ways to mitigate that," Alex said.

Broadening the User Base

Q-CTRL has been serving quantum algorithm developers through Fire Opal, which focuses on error reduction for algorithmic execution, and Boulder Opal, delivering autocalibration for hardware vendors. Since then, the company has built layers of abstraction that expand the range of people it can serve, including domain experts such as chemistry analysts and optimization specialists who can submit data in formats familiar to them.

Application developers represent another audience. Companies including ColibriTD in France and Qunova Computing in South Korea have integrated their own applications on top of Q-CTRL's core pipeline and reported improved performance. The company's education platform, Black Opal, meanwhile, targets traditional software developers who are interested in quantum and continues to drive adoption.

Toward Mainstream Integration

Looking ahead, Alex expressed confidence that quantum will integrate into enterprise workflows and production technology stacks. He pointed to Q-CTRL's work with Riken in Japan, home to the Fugaku supercomputer, an IBM Quantum System Two and a Quantinuum system, as an example of exploring hybrid workflows that combine quantum and classical compute.

Based on hardware vendor roadmaps and the company's experience with customer problems, Alex sees commercial quantum computing advantage in just a few years,   following the company's recent demonstration of practical quantum advantage. "We have confidence that in the next two to three years, we will see meaningful quantum advantage in specific workflows, especially around optimization," he said. He cited a case study with Mazda, where quantum methods required five times less training data to reach a vehicle frame design the company had previously optimized with best-in-class tools, translating directly into reduced research and development cost.

The TechArena Take

Quantum computing has long carried the reputation of a technology perpetually a few years away. What stands out in Q-CTRL's approach is its insistence on accessibility and deployment. By positioning software as the connective layer that brings hardware, control electronics and classical infrastructure together, the company is helping shorten a development cycle that has historically demanded teams of specialists and significant capital. For technology decision-makers, the practical takeaway is that quantum commercial readiness may hinge less on any single hardware breakthrough and more on the software and integration work that makes these systems performant at scale.

Learn more about Q-CTRL at q-ctrl.com, or watch the full podcast episode for more information.

The world tends to measure artificial intelligence (AI) progress in two units: the number of graphics processing units (GPUs) deployed and the megawatts they consume. Yet the people who actually build these systems know that the GPU count is only one part of a much larger engineering effort. Power delivery, cooling, networking, and storage all have to come together before a single accelerator does useful work, and the discipline of fitting those pieces together is becoming one of the most consequential roles in the data center.

On a recent TechArena Data Insights episode, Solidigm’s Jeneice Wnorowski and I explored that hidden layer of the AI buildout with Hitesh Kumar, a GPU cluster architect at Nebius, an AI cloud company that has been drawing attention across the neocloud landscape. Our conversation moved from custom hardware and proprietary software to power heavy AI workloads.  

The System Beyond the GPU

Hitesh described his role as starting once a site has been selected and power secured, and ending when responsibility passes to the logistics and deployment teams. That gap, he explained, is where his team plans clusters, maps them onto floor plans, and decides what connects to what. The work is deliberately broad, and much of it has little to do with the accelerators themselves.

“My job as a GPU cluster architect focuses on a lot of things that aren’t GPUs,” he said. Power and cooling are a major part of the discussion, and so are networking, storage, and the management plane that orchestrates everything. In fact, it’s only after that supporting infrastructure is planned that the headline GPU count comes back into the picture.

Storage and Interconnects Move to the Foreground

Storage has always mattered for training AI models, as data must reach the GPUs fast enough to avoid stalls. Hitesh noted that inference, or running those models in production, is now creating fresh demand beyond that baseline. With a large language model (LLM) chat bot for example, the full state of a long conversation increasingly needs to move off the GPU, and sometimes off the server entirely, with storage acting as an intermediate tier between what a GPU holds close and what it will need soon.

Interconnects are evolving in a similar trajectory. Hitesh traced a path from today’s pluggable optics that enable a connection to one to two cables toward denser designs with tens of ports per unit, and eventually toward co-packaged optics that place optical engines directly in the server. Each step raises new challenges in cooling, cabling, and supply chain readiness that operators must manage alongside the mature technology they rely on today.

The Hidden Costs of Scale

Adding GPUs sounds simple, but Hitesh pointed to two realities that catch teams off guard. The first is component failure. Citing Meta’s published study of a 16,000 GPU cluster, he noted that Meta’s team experienced a failure every three hours on average. At that rate, software must become genuinely fault tolerant, and operations, spares, and logistics all have to scale to match.

The second unanticipated challenge is power behavior. Hitesh highlighted the scale of the power that clusters now draw, and he described how a checkpoint pause can drop a rack tens of kilowatts in moments. Swings that large can affect the grid, pushing operators to consider capacitors, batteries, or software mitigations that rarely make the headlines.

Building Smart and Looking Ahead

For organizations early in their AI journey, Hitesh offered valuable guidance. Going from zero to one, he said, “you really don’t want to be thinking about buying your own infrastructure,” given the high startup costs and overhead. Cloud resources make sense first, followed by colocation, and eventually a dedicated site once demand justifies it.

Looking forward, he expects steady efficiency gains, with “every part of your stack” delivering more performance per dollar. The change he finds most interesting is in network topology. He anticipates clusters organized as “lots of small islands of very tightly connected GPUs,” linked by sparser scale-out and scale-across fabrics. He sees the same fractal pattern emerging across NVIDIA rack-scale designs, Google’s tensor processing units (TPUs), and Huawei’s accelerators alike.

The TechArena Take

Hitesh’s perspective provides a useful counterpoint to conversations that reduce AI infrastructure to a single metric. The teams that succeed will treat power, cooling, storage, and networking as first-class design decisions rather than afterthoughts, and will scale their operational maturity in step with their hardware. As models grow and inference proliferates, the competitive advantage will belong to operators who understand the entire system. For decision makers planning their own buildouts, that full-system discipline is no longer optional.

If you want to learn more about Nebius visit https://nebius.com/

ASAPP SVP Priya Sawant joins the Data Insights podcast to discuss platform engineering, AI agent observability, and how engineering teams maintain trust while building with probabilistic technology.

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Having established a strong position in the AI infrastructure conversation based on its solid-state storage solutions, Solidigm is now broadening that foundation, and its latest push may come as a surprise to those who haven’t been paying close attention. The company recently stepped into AI software with the launch of its Luceta AI Software Suite. On a recent episode of the Data Insights podcast, Jeniece Wnorowski and I spoke with A.J. Camber, VP and GM of AI software at Solidigm, about Luceta, which is designed to make computer vision AI accessible to industrial teams either without deep data science expertise or with an overloaded data science staff.

From Storage to Software

The path to the creation of an AI software business unit within a storage company reflects how much the AI landscape has shifted. A.J. previously spent two years running Solidigm’s strategy team, where Luceta began as an incubation project in an organization focused on long-term growth. In December, the company formalized the project as its own business unit based on the progress seen to date. Solidigm’s vantage point as a storage provider, particularly with its high-density quad-level cell (QLC) drives, gave the team a clear window into where data volumes were growing fastest: cameras.

“Cameras can generate terabytes of video, and that’s just with a single inspection point,” A.J. said. “Specifically as [industries] move into physical AI and other areas, we see computer vision really is the place for innovation right now.”

The Data Scientist Gap

A core premise behind Luceta is that the keenly felt shortage of data scientists is not going away. A.J. cited Bureau of Labor Statistics estimates suggesting that in 2026 demand for data science talent may be as many as ~11 million roles, while the number of people trained to fill those roles is only around one million. Compounding the problem, most existing data scientists are concentrated in about 20 large companies, leaving manufacturers, industrial operators, and others largely without access to this expertise.

Luceta is designed to close that gap by enabling domain experts, such as the line operators who know what a defect looks like, to build and iterate on AI models themselves. The platform automates technically demanding steps in the model-building process, including data diversification, the process of increasing the variety within a dataset to improve model performance.

A.J. estimated this step alone in the model building process would traditionally take a skilled data scientist working with a fairly large dataset roughly 20 hours per model. And a skilled data scientist has been required, because just adding more data blindly to a dataset can decrease model quality, make models slower, and make them more expensive to run. Now with Luceta, however, this step is automated by a data agent that automatically filters, groups, and annotates images, turning raw data into the needed labeled datasets.

“We aim to democratize and enable industrial engineers or mechanical engineers to do these sorts of things,” he said. “With our tool, we do that in the background for you.”

From Pilots to Production

The data quality challenge extends beyond simply having enough variety in a training set. A model that performs well under controlled circumstances often degrades when exposed to the variation and imperfection of an actual production environment, and that is where many AI initiatives stall. Traditional optical inspection systems are rigid by design, but AI-based approaches can adapt as conditions change, provided they are built on the right data and continuously refined.

A.J. noted that ongoing iteration with representative, real-world data is what separates projects that scale from those that never move beyond a pilot. Luceta is built to make that iteration accessible, steering users away from the shortcut of relying on pre-trained models that were not built for their specific environment or use case.

“We don’t think that our customers will be as successful as they can be if they’re just using pre-trained models,” A.J. said. “We want you to use your own data and to iterate on that tool so that not only is it tailored to your use case, but it builds trust with the people using it.”

Seeing It in Action

To demonstrate Luceta’s capabilities and ease of use, A.J. walked us through a live demo. The scenario was a packaging content inspection use case, the kind of application common in large retail or warehouse environments where the contents of a box must be verified before it ships.

Starting from a live camera stream pointed at a small collection of nuts and bolts, A.J. bookmarked a frame, annotated two objects, and allowed the platform to propagate those annotations across additional images automatically. From there, he configured an object detection profile, set a preference for minimizing false positives, and initiated model creation. Within moments, the model was running against the live feed, correctly identifying nuts and bolts with associated confidence scores, and appropriately returning no result when presented with an unrelated object.

The TechArena Take

Solidigm’s expansion into computer vision software may not be the expected move, but the reasoning behind it is sound. As physical AI and robotics move from research labs into warehouses, logistics facilities, and field operations, the demand for computer vision tools that can be trained quickly on real-world conditions is only going to grow. The enterprises best positioned to take advantage of that shift will be the ones that put capable tools in their employees hands to assist human judgment.

Luceta is a practical step in that direction, and for technology decision makers evaluating where AI investments can deliver measurable returns without long implementation cycles, it is worth a closer look. For more information, watch the full podcast, reach out to Solidigm’s industrial AI team at industrialaisw@solidigm.com, or visit solidigm.com.

DigitalOcean's Dan Brown and Solidigm's Ty MacAdam join Data Insights to unpack how storage infrastructure — not just GPUs — drives real AI performance, covering data velocity, cluster density, and DigitalOcean's new Inference Router.

Quantum computing has been a fixture of technology conversations for years, but it has lived mostly in the realm of research announcements and conference speculation. For enterprise IT leaders focused on running production systems, it has rarely felt like an immediate concern. That is starting to shift. In a recent conversation with Solidigm’s Jeniece Wnorowski and Doug Finke, chief content officer at Global Quantum Intelligence (GQI), Doug offered his perspective on where the quantum industry stands, what is driving its transition toward commercialization, and what technology decision makers should be paying attention to before the technology arrives on their doorstep.

An Industry in Its Adolescence

Doug has been tracking the quantum space since 2015 through his publication quantumcomputingreport.com, and he brings an analyst’s discipline to a field that can generate more enthusiasm than clarity. His assessment of the current moment is measured but optimistic.

“It’s probably graduated from the childhood to the teenage years,” Doug said, describing the industry’s maturity. “The progress is really accelerating. We’re seeing much, much more funding now, many more technical papers, patents, those types of things.”

The industry is in what Doug characterizes as a transition zone between research and commercialization. He noted that the quantum ecosystem tracked by GQI currently numbers in the hundreds of companies, and that a likely sign of industry maturity will be a winnowing number of players in the field. He pointed to some of the biggest industries of the 20^th century, including automobiles and semiconductors, as proof: between acquisitions, mergers, and certain technologies winning the race to commercialization, the result of mature technology fields tends to be a smaller, strong number of players.

The Error Correction Inflection Point

According to Doug, the arrival of error-corrected quantum computers on the market will likely represent the single most important inflection point for the industry. To understand why, it helps to know where things stand today.

The current generation of quantum systems operates under a framework called NISQ, short for Near-term Intermediate Scale Quantum. These are machines that run without error correction, and that limitation is consequential. Quantum hardware is inherently prone to errors at rates far higher than classical semiconductors, and this constrains the range of problems that can be reliably solved. Researchers have developed techniques to bundle multiple physical qubits together into what are called logical qubits, dramatically reducing error rates, but systems that use this approach at meaningful scale are not yet commercially available.

Doug expects that in the near term, a small number of applications will reach production through the efforts of highly skilled engineers who can work around current hardware limitations. But the broader expansion of commercial use cases, what he describes as the “knee of the curve,” will depend on error-corrected systems becoming widely accessible.

“That’s probably maybe three or four years from now that you’ll start seeing significant ones with enough qubits that can do real work,” he said.

Software Deserves More Attention

One area that Doug believes receives insufficient attention in mainstream coverage is software efficiency. While most reporting on quantum progress focuses on qubit counts and hardware fidelity, advances in software are quietly expanding what existing systems can do. Researchers are developing techniques that reduce the number of quantum operations required to solve a given problem, effectively stretching the capability of current hardware.

To measure hardware capability, GQI and others in the industry use a unit of measure called a QUOP, or quantum operation, to quantify how many millions of successful operations a system can perform before encountering an error. The goal on the software side is to accomplish the same computational work with fewer of those operations.

“What some of these folks are doing in software, which I think is really impressive, is they're taking software…and figuring out a way to create it so it’s more efficient, smarter, and can actually solve the same problem with fewer quantum operations,” Doug said, pointing to recent work from both Google and researchers at Caltech as examples.

The Cybersecurity Risk CIOs Cannot Ignore

Perhaps the most urgent near-term message for technology leaders concerns cryptographic risk. Quantum computers capable of running Shor’s algorithm will eventually be able to break the RSA encryption that underpins much of today’s internet security infrastructure. That creates a real planning obligation for enterprise security teams, even if the threat is still years away.

“They need to pay very, very close attention to that because they’re going to have to do a lot of work in their IT infrastructure to find all the areas where they’re using asymmetrical cryptography for key distribution, and they’re going to have to upgrade those areas,” Doug said.

The TechArena Take

Quantum computing is no longer a subject that IT leaders can safely defer to the research team. Vendor landscape consolidation, error correction timelines, and cybersecurity posture reviews are all conversations that belong in strategic planning now. Doug’s perspective offers advance notice for chief technology and security officers that the future is coming, and the best course of action is to begin preparing for it today.

For more information, watch the full podcast or visit quantumcomputingreport.com

Enterprise AI has moved beyond the pilot stage. Boards want returns. Businesses want production systems. And somewhere in the middle, technologists are working to build the infrastructure that makes all of it possible without sacrificing the rigor that regulated industries demand. Solidigm’s Jeniece Wnorowski and I recently spoke to Adity Dokania, director of cloud infrastructure and security at Kensho Technologies, an AI and data analytics company owned by financial giant S&P Global. From her seat in the industry, she’s dealing with the opportunities and the complexities of AI adoption daily as she brings frontier capabilities to work for Kensho’s enterprise clients.

From Exploration to Accountability

The place where Adity has observed the greatest change among enterprise clients using AI in the past year is not in their technologies, but their attitudes. Twelve months ago, organizations were largely in exploration mode, running pilots and even learning the vocabulary of AI. Today, the questions have changed. “The conversation has moved from ‘is it even possible’ to being accountable,” she said. “Boards are asking for ROIs. Businesses are asking for something within production.”

That shift is healthy, she notes, but it has created real pressure. The governance frameworks and infrastructure required to support production-grade AI are still being built in most organizations. The result is a tension between leadership pushing for speed and technical and risk teams laying the groundwork to do things correctly for successful ecosystem deployments.

The Friction Points Holding Enterprises Back

When asked where AI adoption runs into the most friction, Adity pointed to three interconnected challenges: data quality, infrastructure readiness, and organizational structure.

In terms of data quality, she clarified that the main issue wasn’t one of data cleanliness, but of trust. “I mean data that’s trusted, that has lineage, that people inside the organization actually agree upon,” she said. For infrastructure readiness, legacy systems were not built for AI workloads, and retrofitting them creates complexity that slows progress. But beyond technology, Adity argued that organizational boundaries may be the most underappreciated obstacle. “AI doesn’t fit neatly into existing team boundaries. It requires collaboration between legal, compliance, business, and engineering, all of them coming together simultaneously.”

That structural challenge, she believes, is what most often determines how quickly something can actually reach the market.

A Framework for Readiness

Adity has developed a practical framework for evaluating when an AI capability is ready to move from experimentation into production. She applies three tests. The first is observability: can the system be monitored after deployment? The second is explainability: even if the system is non-deterministic, can you trace why it reached a particular decision? The third is resilience: “What happens when the system is wrong?” she asked. “Because it will be wrong sometimes. The question is whether it fails in a way that’s recoverable and detectable.”

This framework that Adity uses to evaluate technologies actually predates AI. In an earlier iteration, explainability was instead reproducibility, a quality which is not achievable with non-deterministic generative AI models that by design will not give the same answer twice. Thanks to this history, the framework reflects maturity in thinking about AI risk. Rather than asking whether a system can be made perfect, Adity asks whether it can be observed, understood, and its failures managed.

Watching for Real Signals

Looking toward the rest of 2026, Adity is focused on two indicators that would signal AI maturity across the enterprise market. The first is whether AI begins appearing in operating metrics rather than project reports. When a chief financial officer references AI-driven efficiency on an earnings call as a current contributor rather than a future initiative, something has genuinely changed.

The second signal is hiring patterns, particularly “when enterprises start hiring for AI operations and AI governance roles, not just data scientists and engineers,” she said. The latter reflects a shift in focus around governance and around operational AI. Both indicators point to the same underlying shift: AI moving from a discrete initiative into the fabric of how businesses actually run.

Building the Foundation That Makes AI Sustainable

For Kensho Technologies’ partners and clients, Adity points to three priorities that matter most right now. First is governance infrastructure, the model gateways, observability layers, and audit trails she describes candidly as “the boring stuff that makes everything else sustainable.” Second is use case prioritization, working with leadership to identify where AI can have disproportionate impact rather than pursuing every opportunity at once. Third is integration over replacement, meeting clients where they are and building AI into existing workflows rather than handing them a new interface and expecting them to adapt.

Taken together, these priorities reflect a consistent philosophy: the organizations that will benefit most from AI are not necessarily the ones that move the fastest, but the ones that build the infrastructure to sustain what they build.

The TechArena Take

Adity’s argument that the organizations making real progress in AI deployment are those investing in the scaffolding that makes AI deployable, defensible, and durable uncovers a truth that more and more organizations are coming to embrace. Governance, observability, and cross-functional ownership are the preconditions for AI deployments that actually stick. While an ambitious vision may garner media attention, it’s the “boring” homework that ensures that investments in AI actually pay off for enterprise users.

To learn more, listen to the full podcast or visit kensho.com.

Alex Shih of Q-CTRL discusses quantum computing, AI infrastructure, HPC integration, and the shift from research labs to production systems.

As a technology that promises to revolutionize fields from encryption to drug discovery, quantum computing receives the kind of attention reserved for technologies that promise to change everything. But between the promise and the reality sits a complicated, fragmented landscape of hardware modalities, software frameworks, and developer workflows that still have no clear consensus. Recently, Solidigm’s Jeniece Wnorowski and I spoke with Kanav Setia, co-founder and CEO of qBraid, to explore where the field stands today and what it will take to make quantum computing practical for the organizations that want to leverage it.

A Field Still Finding Its Footing

Kanav came to quantum computing through a PhD in theoretical physics at Dartmouth College, where exposure to quantum mechanics led him toward quantum algorithms. When he started his PhD, he noted, there were no freely available quantum computers. Then about a decade ago, IBM made a two-to-five qubit machine publicly available. From that start, today systems are coming online regularly reaching 100 or more qubits.

Yet this progress, while real, has not resolved the fundamental question of what kind of hardware will win. While in classical computing silicon transistors underpin every device, quantum computing’s base infrastructure is still in flux, with many approaches to building “qubits” (quantum bits) in use. Superconducting circuits, neutral atoms, trapped ions, and photons all represent viable approaches, each with different trade-offs.

“If you are an end user utilizing quantum computers, you will be looking at different technologies,” Kanav explained. “And when you write your algorithms, you would want to run them on all the different kinds of hardware because you don’t know which of the hardware is going to perform the best.”

The Interoperability Problem

That diversity creates an immediate practical challenge for developers. Writing a quantum algorithm is only the beginning. Getting it to run reliably across different quantum processors requires navigating a maze of compilation layers, framework dependencies, and hardware-specific pipelines that do not yet work together cleanly.

“You write your algorithm once, and then you run it through one quantum computer pipeline,” Kanav said. “You need to make sure that the algorithm is supported by that quantum computer throughout the various frameworks. And many times, when you try out different algorithms, they have their own repositories, which are managed by certain companies that only support different hardware. So you have to zigzag to the end of the chain, which allows you to run on a quantum computer.”

qBraid’s platform addresses this directly by providing unified access to hardware from multiple providers alongside software from across the ecosystem. The goal is to let a developer come to one place and reach the full industry without spending time on integration work that does not advance their actual research or application.

Software as a Force Multiplier

While quantum computing hardware has seen significant advances in recent years, Kanav was clear that algorithmic and software progress can be a powerful accelerant. Early estimates suggested that breaking modern encryption would require billions of qubits running for years. Successive rounds of algorithmic improvement brought that estimate down to 20 million qubits, and then recently to around 10,000.

“All of those are algorithmic improvements along with a lot of software improvements,” Kanav noted. The lesson for enterprises is that the software layer is not a secondary concern waiting for hardware to mature. It is actively shaping what becomes possible and when.

qBraid is also working on what it calls QBraid OS, which aims to orchestrate the hybrid workflows that real quantum applications require. Because today’s quantum processors are error-prone, they depend heavily on classical compute for error correction and for handling portions of a computation better suited to central processing units or graphics processing units. Getting those workloads to move across processor types seamlessly, and to return coherent results, is the kind of infrastructure problem that will need to be solved before enterprises can rely on quantum for anything consequential.

What Signals Maturity

For organizations watching the space, Kanav offered a guideline for understanding when quantum will be ready for broader adoption. The key hardware milestone is error rates dropping below a threshold where results can be trusted without extensive post-processing. “Once it starts doing that…you will be sure that quantum computers are good enough to break encryption,” he said, “which means you will need to update all of the encryption infrastructure that we use.”

Beyond that threshold, the parallel question is how quickly new applications will emerge that justify the investment. Kanav drew a comparison to the early days of machine learning, when the relative merits of CPUs and GPUs were still being debated. The right architecture for certain workloads only became obvious over time, and the same pattern is likely to play out in quantum.

Drug molecule design, where quantum mechanics is directly relevant to simulating molecular behavior, is one area where intuition suggests quantum should eventually outperform classical approaches. But as Kanav was careful to point out, intuition is not proof.

The TechArena Take

Quantum computing remains early-stage, but the infrastructure decisions being made now will shape which organizations are positioned to benefit when the field matures. qBraid’s approach, standardizing access across hardware and software, while building the orchestration layer that hybrid classical-quantum workflows require, is creating the needed bridge to enable developers to focus on solving problems rather than managing their complexity. Technology decision makers who begin to leverage such platforms now will be far better positioned to move quickly when quantum hardware crosses the threshold from experimental to reliable.

To learn more, listen to our full podcast episode or visit qbraid.com.

Forty years after a graduate student set out to test whether currents and voltages in a hand-built circuit could obey the rules of quantum mechanics, that same researcher now holds a Nobel Prize and runs a startup focused on a less romantic problem: manufacturing quantum computers at scale. My recent conversation in Santa Barbara with 2025 Nobel laureate John Martinis, co-founder of Qolab, and ZeroPoint’s Nilesh Shah made it clear that quantum’s next revolution will be built in fabs and data centers. My co-host for this Data Insights episode was Solidigm’s Jeniece Wnorowski.

From a 1980s Thesis to a Nobel Prize

John’s Nobel Prize traces back to a graduate-school question. As a thesis student in the early-to-mid 1980s, working alongside his co-winners John Clarke and postdoc Michel Devoret, he set out to test something foundational: whether a macroscopic variable, say currents and voltages in a circuit, could obey the laws of quantum mechanics. They did. The applied vision came much later.  

“It's natural to think about building an electrical quantum computer, because the computers that we use today are based on electrical circuits,” said John.

What Quantum Supremacy Really Means

In 2019, John’s team at Google published results from the Sycamore quantum processor, completing in 200 seconds a calculation estimated to require 10,000 years on classical hardware. Better classical algorithms have closed some of that gap, but the underlying argument holds.  

“Some people don’t like the word supremacy, and want to say quantum advantage,” he said. “But I’ve always said supremacy because of this fact. It’s not just a little bit better, but it’s kind of exponentially better in that as you make the systems bigger, it just becomes hopeless to try to simulate that with a regular computer.”

From Golden Chandelier to Integrated Circuit

If 2019 settled the science, the next phase is harder. Today’s quantum machines are still hand-built lab instruments.  

“If you look at the quantum computers now, I call it the golden chandelier,” John said, beautiful in their tangle of microwave components and wiring, but not mass producible.

He sees a clear historical analogue. The plan is to do for quantum what semiconductor manufacturing did for classical computers in the 1950s and 60s: trade the mess of wires for the integrated circuit. To get there, Qolab is working with semiconductor partners on better fab processes and chip-scale packaging. A million-qubit machine built with current techniques would run into the tens of billions of dollars. His bet is that fab-scale manufacturing will bring the cost down the same way it did for chips.

Where Quantum Meets the AI Data Center

Nilesh pulled the conversation back to today’s AI build-out, where hyperscaler economics are denominated in power. As data center usage is increasingly measured in gigawatts, energy becomes the finite variable. In this scenario, quantum has to earn its place in three ways: improving power efficiency, cutting latency, or unlocking stranded data center capacity in legacy infrastructure.  

Nilesh also flipped the dependency most people assume: today, classical infrastructure carries quantum, not the other way around.  

“Today it’s actually quantum computing that is leveraging the power of AI and GPUs and CPUs to make these quantum computing elements more stable,” he said, pointing to real-time error correction, system-level calibration, and the digital twins that researchers run alongside live quantum experiments.

One hyperscaler’s multi-million-dollar quantum rentals last year drove a much larger demand for classical compute, in terms of storage, memory and processors needed to feed quantum applications.

When the Inflection Arrives

Asked when quantum’s ChatGPT-style moment might arrive, John didn’t hedge.  

“The standard quote I give is 5 to 10 years,” he said, “with the caveat that people are being very optimistic about the system engineering challenges. We have a little bit of time, but you have to start working on it right now.”

Nilesh framed the wait through the lens of risk.  

“The question that people are asking is not should I invest in it, but rather, can we afford not to invest?” He layered in the geopolitical reality, noting that quantum has become a national mandate well beyond the U.S., with sovereign investment accelerating across Europe, Canada, China, and Australia.

Advice for the Next Generation

When I asked what students considering scientific careers should focus on, John leaned into his role as an educator at UC Santa Barbara. The physics courses are still essential, but engineering courses are just as important: programming, microwaves, optics, and packaging are all core to building real systems, and the field rewards range. He pointed to a former Google colleague who came in with a software background, learned quantum on his own, and “started breaking these limits that everyone put because they made wrong assumptions on it.”

Nilesh, whose own first quantum experiments came through Intel’s open-source qHIPSTER simulator and an early five-qubit IBM machine, agreed.  

“It is a multi-disciplinary type of a field,” he said, urging the industry to do a better job of communicating quantum’s career opportunities to students who today gravitate toward machine learning and large language models.

The TechArena Take

Quantum computing’s first revolution was scientific. Its next, as this conversation made clear, will be built in fabs and data centers. The question is no longer whether the physics works, but rather whether the supply chain and economics can keep pace with what science has proven possible.

For data center leaders, quantum is already pulling demand for classical infrastructure, and preparing for the inflection point, be it across cryptography, materials science, drug discovery, or AI-adjacent workloads, is no longer optional. The timeline may be uncertain, but not the need to start.

Listen to the full Data Insights episode on TechArena.ai.

The race to build AI infrastructure is defined, in most coverage, by megawatts announced, leases signed, and capital deployed. What gets less attention is whether any of it actually gets built on time. Atif Ansar, co-founder of Foresight Works, has spent 15 years studying why large projects fail, including through academic work at Oxford University. In my recent conversation with him and Solidigm’s Jeniece Wnorowski, Atif shared an insight from that work that shapes his company’s direction: the limiting factor in the AI data center boom is not technological, but human.

Foresight Works offers a project delivery platform combining AI, proprietary data, and scheduling methodologies developed through Atif’s academic research. The platform is built around the idea that projects are human systems, and that basic psychological biases that can harm outcomes can be overcome with processes that ensure both micro daily commitments and larger goals are met.

The Real Bubble Risk

In discussion about a potential AI infrastructure bubble, the concern usually voiced is about overbuilding. Will the supply of new data center capacity outpace demand? Atif rejects that framing. He points to accelerating demand for AI-enabled services and the contractual structure of hyperscaler leases as evidence that demand is not the problem. The problem, he argues, is delivery.

“These data centers are being built under contract from very high rated companies like hyperscalers,” he explained. “And as a result, they have 15-year leases. So the developers are not taking huge amount of risk.” But that contractual protection cuts both ways. A single month of delay on a 100-megawatt facility can cost $15 million or more in lost early revenue, and service level agreement penalties from hyperscalers compound the damage further. Atif estimates that more than a three-month delay can dissipate the net present value of even a very large build. “The penalties for missing your targets become potentially insolvency causing for companies that are not well managed,” he said.

The Watermelon Problem

One of the more memorable concepts Atif brought to this conversation was what he called the watermelon problem, a phrase for projects that are “green from the outside until they suddenly turn red.” Atif noted that the gap between perceived and actual progress is typically a product of optimism bias and political pressure, not malice. Teams hope they will catch up. But often, they do not.

The interdependencies inside a data center build make this particularly dangerous. A purchase order that should have been issued today, but was not, means a missing piece of equipment six months from now. The delay is invisible until the moment it becomes a crisis. “What lets people down is the accumulation of these very small variances,” he noted. “It’s actually those micro details that make the difference.”

Foresight is designed to function as a control tower, allowing organizations to manage the macro picture while having a handle on micro variances early enough to act on them. In one case, the platform uncovered a five-month delay buried beneath distorted reporting.

Where Schedules Break Down

Atif has identified recurring patterns in how project schedules fail. One is an overemphasis on civil and structural work, which is visually dramatic and easy to represent, at the expense of mechanical, electrical, and plumbing work, which is complex and harder to schedule. “Nearly 60 to 80% of a data center is MEP,” he noted. Treating it in broad strokes rather than mapping it with precision is a reliable path to delay. A second pattern involves compressing commissioning into the final phase of a build, when factory and site acceptance tests should begin far earlier, alongside procurement and equipment delivery.

Foresight’s AI layer helps identify structural gaps in submitted schedules, particularly missing milestones or poorly sequenced dependencies. “We help them look at what milestones they are likely missing or what gates they’re likely missing and help them insert those back in the appropriate places,” Atif said.

A Skills Supply Crisis Beneath the Capital Crisis

The execution problem extends well beyond data centers. Digital infrastructure, the energy transition, and defense spending are all generating enormous project delivery demands simultaneously on top of existing demand from evergreen sectors like pharmaceuticals and civil infrastructure, and the construction workforce is not keeping pace. Atif estimates that data centers represent roughly 10% of the global construction market by value, yet the people working in the industry amount to just 0.05% of the global construction labor force.

“So it’s still a cottage industry in terms of the footprint,” he said. “They need a lot of technology, automation, and AI to simply keep pace. They also need education…and I think that we need to upskill people and train them in the art of becoming better project managers.”

The TechArena Take

While day-to-day execution doesn’t typically garner headlines, Atif's work makes a compelling case that it deserves far more attention from executives committing capital to AI infrastructure. The trillion-dollar build underway globally requires disciplined upfront planning, clear governance, and repeatable processes. The developers who get this right are those who invest in process maturity early, building the kind of credibility that holds up with communities, investors, and hyperscalers alike. Platforms like Foresight Works represent an important step toward making that discipline accessible at scale, at a moment when the cost of getting it wrong compounds with every month of delay.

To learn more, listen to the full podcast or visit foresight.works.

Sustainability, when it even enters the conversation for data centers today, means different things to different people. It is a broad topic, including energy use, water use, e-waste, and the effects of rapid growth. Gabriel Lazar, head of sustainability at Submer, a  Barcelona-based full-stack data center company, sees a need for both greater specificity and more comprehensive systems thinking as part of organizational strategies.

In a recent discussion with Gabriel and Solidigm’s Ace Stryker, Gabriel laid out a pragmatic framework for thinking about data center impact: one that spans energy, community relationships, and the uncomfortable question of whether the industry is using what it builds.

The Cost of Growth Without Durability

The data center industry is struggling with sustainability, and Gabriel is direct about it. About half of data center operators don’t even have sustainability plans in place. Many companies that do have them are still moving in the wrong direction. Part of the difficulty is the recent push for higher performance and scale hasn’t considered what will make what is being built now durable for years to come.

What the industry needs, Gabriel argued, is a genuine reduction in resource intensity, pursued with enough specificity to actually be achieved. He expressed optimism that data centers and digital infrastructure can continue to have a smaller environmental footprint than heavy industries like steel making or car manufacturing. . Getting there requires separating growth from impact, finding ways to scale the industry while bending the resource curve downward. “We need to start seeing that graph divide, with impact going down and the growth going up.”

Thinking in Systems, Not Silos

One reason sustainability conversations fall short, Gabriel argued, is that they tend to stay within the boundaries of the sustainability field itself. The forces shaping data center infrastructure today, including geopolitical risk, supply chain scarcity, energy market structures, and community relations, do not respect those boundaries. Addressing them requires a multi-disciplinary approach that borrows from fields with more practice at managing complexity.

“If we were to pick a field that is best at doing this right now, it’s probably risk, and more specifically, if you look at insurance companies, they’re amazing at capturing this because they have so much data and they’re able to crunch it,” he said. Having people from different backgrounds collaborating and communicating is the best path to addressing the extremely complex, interconnected challenges such as power shortages that the industry is facing today.

Heat Reuse: Valuable, but Not Universal

Gabriel leads a heat reuse workstream for the Open Compute Project, so his cautious take on the topic may come as a surprise. The logic of heat reuse is straightforward: nearly all the energy flowing into a data center converts to heat, and using that heat for other purposes rather than dissipating it is sensible in principle. In practice, though, the economics depend heavily on geography and local infrastructure.

District heating networks, common in Germany and other northern European countries, make heat reuse viable at scale. In Spain, where Submer is headquartered, or in the United States, those networks largely do not exist. Trying to mandate heat reuse uniformly across markets will produce uneven and often poor results.

“It might just be 20% of data centers worldwide that are applicable,” Gabriel said. “I think that makes more sense than trying to hit it all and not getting anything.”

He flagged desalination and water treatment as underexamined applications for heat reuse, noting their round-the-clock demand profile. The constraint, again, is proximity: only data centers sited near coastlines or water treatment facilities would benefit. The pattern across all of these examples is the same: broad ambitions need to be matched with specific, locally grounded analysis before they translate into real outcomes.

The Community Dimension

Beyond grid dynamics and thermodynamics, Gabriel emphasized that sustainable infrastructure is also a social proposition, and one that requires the same place-specific thinking. Heat reuse, for all its complexity, offers something rare in the industry: a tangible benefit that local communities can see and understand. Unlike grid flexibility programs or ancillary services markets, a data center supplying heat to a nearby manufacturer or supporting a local business is a concrete value proposition to the people who live nearby.

That matters for the permitting process. “It goes a long way for that regulatory process, the buy-in, the social license to operate,” Gabriel said. Operators who engage communities early, rather than as an afterthought or not at all, tend to move faster through approval and run into fewer delays.  This practice also helps with future projects as a reputation builder. The TechArena Take

Gabriel’s argument is ultimately a call for better engineering of the sustainability problem itself. Sustainability as a vague aspiration produces vague results, if any. Sustainability as a set of specific, locally calibrated initiatives focused on relevant topics  can produce real outcomes. Infrastructure should not be considered in isolation, especially not when it plays such a crucial role for societies. In the race to build data centers, systems and longer-term thinking should be key. To learn more about Submer’s work, watch our full podcast or visit submer.com.

As general manager of MiTAC Computing, Raymond Huang is building the case that validated, pre-integrated infrastructure is the best path to AI at scale. His company, long known as a manufacturer of high-performance compute systems, has evolved into a provider of end-to-end AI infrastructure solutions. In a recent TechArena Data Insights episode, Solidigm’s Jeniece Wnorowski and I talked to Raymond about the strategy, the partnerships, and the technical decisions driving that evolution.

The Evolution to Delivering Turnkey AI Capacity

MiTAC’s transformation has been driven by three converging pressures: the technical demands of modern AI workloads, economic pressure on margins and differentiation, and a customer base that increasingly wants faster time to deployment. In response, the company is developing turnkey AI infrastructure solutions built around pre-integrated rack-level systems in which servers, GPUs, networking, power distribution, and cooling are validated together before they ever reach a customer site.

“The AI factory is a new category we're working on,” Raymond said. “MiTAC is aligned around the modular AI cluster as a building block. We don’t just build servers; we deliver AI capacity.”

That positioning matters because the traditional model, in which customers sourced components separately and spent months integrating them, is increasingly untenable at the pace AI demands. Raymond noted that MiTAC’s approach compresses that timeline significantly, moving customers from months of integration to days or weeks of installation.

Solving the Density, Power, and Cooling Equation

AI clusters are constrained by how tightly GPUs can be packed without hitting thermal or power ceilings. MiTAC addresses this through pre-designed high-density rack configurations validated for specific GPU setups, ranging from 32 air-cooled GPUs per rack to 96 GPUs per rack with liquid cooling. This eliminates the trial-and-error phase that often accompanies high-density deployments.

Power delivery is equally central to the approach. Modern GPU racks can draw easily from 30 kilowatts up to 130 kilowatts, and instability in that power supply creates downstream problems that are expensive to diagnose and fix. Because MiTAC controls both the design and manufacturing of its systems, it can pre-match power profiles to specific GPU and CPU configurations from the ground up. That end-to-end ownership allows the company to engineer across voltage options including 208V, 415V, 480V, and potentially the upcoming 800V DC standard.

On cooling, the company has developed direct liquid cooling systems capable of removing up to 95 percent of generated heat via a liquid loop. Raymond pointed to MiTAC’s G4826Z5, a 4U system built around dual AMD EPYC 9005 CPUs and AMD Instinct MI355X GPUs, as a demonstration of what liquid cooling makes possible.

“Without the DLC, this kind of density wouldn’t even be thermally practical,” Raymond said. “The system isn’t performance throttling even during long AI training runs. So the system sustains peak GPU utilization instead of cycling down due to heat. This is critical to the large language model training or HPC simulation multi-AI workload jobs.”

A Partner Ecosystem Built Around the Full Stack

In our Data Insights series, we often discuss how as AI infrastructure becomes more data intensive, application performance relies on compute and storage working together optimally. Raymond described a set of partnerships that MiTAC has developed to address the layers of the AI stack to enable high performance. For example, with Solidigm and DDN, MiTAC has built a storage solution pairing its servers with Solidigm NVMe drives and DDN software to eliminate the I/O bottlenecks that can leave expensive GPU clusters sitting idle waiting for data.

For orchestration, MiTAC has partnered with Rafay, whose managed Kubernetes platform and GPU orchestration tools simplify cluster management across multi-node environments. The combination allows customers to go from power-on to a usable cluster significantly faster, with centralized policy management that scales consistently from a single rack to 500.

Another collaboration reflects MiTAC’s commitment to reducing the energy footprint of AI infrastructure: its work with Akash Systems using diamond-based cooling. Because diamond conducts heat at roughly five times the rate of copper, the resulting systems consistently run up to 10 degrees Celsius cooler than standard configurations, delivering measurably better performance per watt without increasing energy consumption.

Manufacturing Localization and the Road Ahead

MiTAC is also expanding its North American manufacturing footprint to meet demand for localized supply chains. Raymond described the company’s capacity as several thousand racks per month, with a focus on building, fulfilling, and supporting AI infrastructure locally in each region it serves.

Looking a few years out, Raymond sees data center design converging around liquid cooling as the universal standard, rack densities climbing toward 100 kilowatts to potentially one megawatt per rack, and hybrid on-site power generation becoming common. The organizing principle Raymond returned to is modular, repeatable architecture.

“Think of it like Lego blocks for AI capacity,” he said. “This is extremely critical for deploy to speed needs.”

The TechArena Take

MiTAC’s evolution to an AI infrastructure provider reflects a broader maturation in how the market thinks about deploying AI at scale. Validated, pre-integrated solutions that compress deployment timelines and reduce operational risk are becoming increasingly crucial. Raymond’s message to technology decision makers is straightforward: the competitive question is how quickly and reliably you can put your chosen infrastructure to work. MiTAC understands that customers who face that pressure will value solutions that arrive ready to run.

For more information, listen to the full podcast episode, or visit MiTACcomputing.com.

There’s a long-held idea in enterprise technology that people who make safe, conventional procurement decisions stay employed. Today, that logic is playing out as procurement teams and IT leaders choose dominant global cloud platforms not because they’ve done a thorough evaluation, but because it feels like the default.

Solidigm’s Ace Stryker and I recently sat down with Albane Bruyas, chief operating officer of Scaleway, who made a compelling case that the “safe” choice may come with unexpected risks, and that having a backup plan is critical. With 25 years of cloud experience behind it, Scaleway is working to demonstrate that a homegrown European complement to the global hyperscalers is essential for organizations that care about control, cost transparency, and long-term resilience.

More Than Sovereign: The Case for Full-Stack Control

Scaleway describes itself as both a sovereign European cloud provider and a neocloud built for AI infrastructure. For Albane, however, the word “sovereign” only gets you so far.

“What is very interesting for our clients is that we sell full autonomy,” she said. “We have control on all the value chain. This is what makes the pure difference for our clients.”

That control extends across software, hardware, network transit, and pricing. Scaleway operates within a major French telecommunications conglomerate, which gives the company ownership over connectivity that most cloud providers must outsource. The result, Albane argues, is a level of transparency and pricing stability that organizations cannot get from vendors who depend on third-party components throughout their stack.

Sustainability is also woven into this argument. Because Scaleway built its data centers, primarily in France, it has been able to optimize for energy efficiency. That includes tracking and reducing carbon footprint at the hardware level, a capability made more precise by the company’s participation in the Open Compute Project.

A Procurement Argument Rooted in Risk

Before joining Scaleway, Bruyas worked in industrial procurement, and she brings that lens directly to conversations with enterprise buyers. In industry, she notes, you always ensure you have at least two suppliers for physical goods. Her message is straightforward: operating differently with your digital suppliers doesn’t eliminate risk. It invites it.

“In the digital world, people are just forgetting that their principal strategic supplier is a unique supplier they cannot move out from,” she said. “There is no solution if there is a crash. There is no solution if different prices come out. No solution if there is an external government that asks for data. You have no choice.”

Her advice to organizations anchored to a single hyperscaler is practical, not confrontational: “You will never be fired because you choose one of the scalers. So it’s just like, test an alternative. You need to have one, and you will be happily surprised,” she said.

In a similar spirit, Scaleway’s active participation in the Open Compute Project is not simply a technical preference; it is a supply chain hedge. By building on open hardware standards, the company can source components from multiple vendors, reducing dependence on any single manufacturer and creating competitive pricing leverage.

“If you have the most open hardware you can, then you have the capacity to buy from different suppliers,” Albane explained. “If you have the capacity to buy from different suppliers, you can have a better price, and you can have more capacity because you can go to different places.”

Building for an Agentic AI World

As AI workloads shift from simple inference queries toward longer, more complex agentic workflows, the infrastructure requirements change substantially. More context, more memory, tighter latency budgets, and greater demand for diverse compute options are all part of that transition.

Scaleway’s strategy is, once again, to embrace sourcing from different providers. The company offers multiple GPU types, is actively collaborating with next-generation chip designers, and maintains capacity for CPU-based inference where appropriate. Albane noted that Scaleway has a history of being early to emerging architectures, having been among the first providers to offer Arm-based servers.

“We want to continue to be really at this level of technology where we can put something in place that nobody has,” she said.

The TechArena Take

Scaleway occupies an unusual position in the cloud market: mature enough to offer a full public cloud catalog, yet structured in a way that gives it operational visibility and pricing control that most providers lack. Albane makes a credible case that full-stack ownership is not just a differentiator in the marketing sense but a concrete operational advantage, particularly for organizations that need cost predictability, data residency assurance, and a genuine second-source option. Enterprise buyers that prioritize control and transparency can benefit by making a strategic choice to diversify their cloud strategy before vendor dependency becomes a liability.

Hitesh Kumar explores AI infrastructure design, GPU clusters, networking, storage, cooling, and scalability. Learn how modern AI systems are evolving beyond GPUs to full-stack architecture.

Enterprise storage has long had a potential pain point with growing unstructured data, but most organizations have tolerated the ballooning storage needs of their documents, video files, images, sensor outputs, project archives, and more because the cost of inaction seemed manageable.

That calculus has changed. The surge in AI workloads has triggered an acute shortage of high-performance memory chips, the same components that underpin enterprise storage hardware. As AI infrastructure competes for that supply, vendors have passed the cost along quickly, leaving organizations to urgently search for new solutions.

Recently I sat down with Solidigm’s Jeniece Wnorowski and Krishna Subramanian, co-founder and chief operating officer of Komprise, about how enterprises can better handle unstructured data, which makes up roughly 90% of all data generated worldwide. We explored how a problem that has been simmering for years has suddenly jumped to the top of to-handle lists for enterprise IT leaders.

The Budget Shock No One Planned For

The numbers are striking. “Just in the first few months of this year, most storage companies raised their prices by anywhere from 30% to about 75%,” Krishna noted. For IT leaders already managing double-digit data growth, that is a significant jolt to infrastructure budgets that were built around very different assumptions. Organizations are being asked to absorb substantially more data while spending far more per unit of capacity, with little room to maneuver.

The problem is compounded by poor visibility. Most enterprise IT teams know they are sitting on large volumes of cold data, files that are no longer actively used but continue to consume expensive primary storage. What they typically lack is the precision to act on it. “You can’t manage what you don’t know,” Krishna said. “Most IT leaders, I think the problem is they don’t know really what the issues are with their unstructured data.”

Unstructured data is sprawling by nature, generated across different users, applications, and systems with no consistent format to make analysis straightforward. Knowing the general shape of a problem is different from knowing which specific data can be safely moved, where it lives across on-premises and cloud environments, and what the real savings opportunity looks like.

Reclaiming Capacity Without Buying More

Komprise designed its Flash Stretch Assessment to close exactly this gap. Available to qualified customers with at least 500 TB of data for no charge, the service analyzes storage environments across both on-premises and cloud infrastructure to produce a concrete picture of data activity, growth patterns, and cost distribution by user, application, and data type.

The goal is to identify cold data that can be moved to lower-cost storage tiers, freeing up primary capacity without requiring new hardware purchases at today’s elevated prices. “We’re freeing up all that space, but you can now put new data onto it,” Krishna said. “You’re kind of reclaiming existing capacity without having to buy at these exorbitant prices.”

When organizations act on the assessment, the financial impact can be significant. Unstructured data accounts for roughly 30% of the average IT storage budget, and that figure encompasses not just primary storage but also backup and disaster recovery copies. Komprise reports that customers who tier their cold data can reduce storage and backup costs for their unstructured data by around 80%.

Transparent Access, Not a Trade-Off

A predictable concern with any tiering strategy is disruption. Moving data to lower-cost storage only delivers value if users and applications can still reach it without friction. Komprise addresses this through its patented Transparent Move Technology, which relocates data to the cloud or another tier while leaving a dynamic link in its original location.

“It looks like the X-ray image is still local,” Krishna explained. “Your applications can still open it, but when they go to open it, we stream that data from the cloud instead of it sitting locally. It’s transparent to users and applications so they don’t see any change.”

For data that follows cyclical patterns, such as project archives that go dormant for months before becoming relevant again, the platform supports bulk recall that restores entire datasets on demand, giving IT teams flexibility to handle the rhythms of how enterprise data actually gets used.

The TechArena Take

The current storage crisis is not a temporary disruption that patient organizations can wait out. Memory chip shortages tied to AI demand will likely ease, but the need to optimize your existing resources instead of planning to scale to meet increasing demand alone is likely the new normal.

The current pressure is forcing a discipline that should have existed all along: Many enterprises have been storing unstructured data without meaningful visibility into what they have, how fast it is growing, or what it is actually costing them. That was always inefficient. The difference now is that the inefficiency has become expensive enough to demand attention. Organizations ready to engage with decisions about what data to keep, where to keep it, and how to organize will be best positioned in the race to improve AI performance, slow infrastructure spending, and increase operational agility.

To learn more, listen to the full podcast or visit komprise.com.

Doug Finke joins an episode of Data Insights to discuss quantum computing, commercialization, cybersecurity risks, and the road to enterprise adoption.

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.

Foresight’s Atif Ansar explains delivery risk, project delays, governance, and how AI can improve large-scale data center buildouts.

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.

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