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When we announced our media partnership with the AI Infra Summit earlier this year, we said we’d be spotlighting the innovations driving the field forward. Our event coverage has already begun, and today we’re putting that work into action in a new way: with the inaugural TechArena Ad Astra AI Infrastructure Competition.

The contest is an open call for companies attending the AI Infra Summit to make their case for why their technology belongs on the shortlist of this year’s most significant innovations in AI infrastructure. One winner and three honorable mentions will be selected by a panel of TechArena Advisors and will be featured on our Ad Astra Showcase page.

The winning company also receives a suite of content opportunities to amplify their story before, during, and after the summit in Santa Clara this September, including the following assets:

• 5 Fast Facts Interview Blog: A Q&A published on the TechArena Forum focused on AI infrastructure requirements with select answers featured in TechArena's 2026 AI Infrastructure Requirements Report
• Solution Brief: A two-page digital solution brief developed by TechArena for the winning company to use during and after the event
• Recap Blog Mention: A mention of the winning company’s innovations in TechArena’s recap blog published during the event
• Post-event Key Insights Blog: A contributed blog by the winning company published on the TechArena platform capturing the company’s key insights from the show

Submissions are judged on three criteria:

Alignment to one of the summit's focus areas (Compute, AI Data Center, Data Movement, Data & Models, or Physical AI)

Evidence of real-world impact backed by measurable outcomes

Clarity of communication

Entries are 200 words. The contest is open now through July 22 at 5 PM PDT, and is open to any company with at least one employee attending the AI Infra Summit in person.

If your company is doing work worth talking about, this is the place to say so. Submit your entry here.

Free Tickets and Discounts from TechArena

If you haven’t yet registered for the AI Infra Summit, now is the time to act! As part of our partnership, we’re pleased to offer benefits to our TechArena followers who want to join the conversation in Santa Clara this fall:

Free expo tickets (valued at $447) are available to qualified individuals working within the AI infrastructure field. Apply for free expo tickets.

A registration discount is available for full access and VIP tickets. Quote TECHARENA15 on the registration page to save 15%.

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.

Every year on Women in Engineering Day, I find myself reflecting on how unlikely my journey into technology really was.

I did not grow up surrounded by computers. In fact, my first computer class did not involve a computer at all. Our teacher stood in front of a blackboard and drew a monitor, keyboard, CPU, and mouse with chalk. Those drawings became our introduction to technology. We learned what each component did long before we ever had the opportunity to touch a real machine.

Looking back, it feels almost surreal. The idea that a young girl learning about computers through chalk sketches would one day build a career protecting technology used by millions of people around the world would have sounded impossible. There was no roadmap pointing me toward engineering or cybersecurity. What I did have, however, was curiosity. I wanted to understand how things worked, why problems existed, and how they could be solved. Long before I had access to technology, I had a desire to learn. In hindsight, that mattered far more.

A Career Without a Master Plan

One of the biggest myths about successful careers is that people know exactly where they are going from the beginning. My experience was very different.

I never had a master plan. My early career moved through operations, analytics, consulting, business analysis, and program management. At times, I worried that my career looked scattered. While others seemed to be building expertise in a single area, I felt like I was constantly learning something new and starting over.

What I could not see at the time was that every experience was preparing me for challenges I had not yet encountered. Consulting taught me how to communicate. Operations taught me how to execute. Analytics taught me how to think systematically. Program management taught me how to align people around a common goal. Years later, those skills would become some of my greatest strengths as a leader.

Learning to Be Comfortable with Change

For much of my career, change was a constant companion. I moved cities, changed roles, joined new teams, and repeatedly found myself in unfamiliar environments. At the time, every transition felt like starting over.

Looking back, those experiences taught me resilience. They taught me how to adapt, learn quickly, and stay comfortable in uncertainty. The confidence I developed did not come from having all the answers. It came from repeatedly proving to myself that I could figure things out. That lesson would later become invaluable in a field where change is the only constant.

Finding My Next Chapter

Eventually, I was hired as a business program manager in Reno, Nevada. Coming from smaller organizations, I suddenly found myself inside one of the largest technology companies in the world. Reno became my classroom. It was where I learned how large-scale systems operate, how decisions impact millions of users, and how global teams solve incredibly complex problems.

As much as I appreciated the opportunity, I eventually found myself wanting something more. For the first time in my career, I made a deliberate decision about where I wanted to build my future. I wanted to move to Seattle.

I did not know exactly what role I wanted. I simply knew I wanted to be in an environment filled with innovation, growth, and opportunity. So I started applying. Program management, product management, operations, strategy. If the role was in Seattle, I applied. I genuinely believed that if someone gave me an opportunity, I could learn whatever I needed to learn.

The Interview That Introduced Me to Cybersecurity

One of those applications happened to be for a product manager role within a security organization.

To be honest, cybersecurity was not part of my plan. I knew very little about the field. My career had been built around solving business problems, driving execution, and bringing people together to achieve outcomes. Security felt like an entirely different world.

What surprised me during the interview process was that the conversations were not only about technical expertise. They were about problem solving, navigating ambiguity, collaboration, and learning. For the first time, I realized that my unconventional path might actually be an advantage.

I accepted the role without fully understanding how much it would change my life. Looking back, that interview became one of the defining moments of my career. I was searching for a new city, but along the way I discovered a profession and a purpose.

Finding My Place in Cybersecurity  

When I joined the security organization, the learning curve was steep. There were technical discussions I struggled to follow, acronyms I had never heard before, and moments when I wondered whether I belonged at all. Many of my colleagues had traditional security backgrounds, while I was still learning the language of the industry.

For years, I questioned whether cybersecurity was truly where I belonged. Yet every time I considered moving on, another challenge appeared. Another problem needed solving. Another opportunity to make an impact emerged.

Slowly, the field that once intimidated me began to inspire me. What started as a job gradually became a purpose. Looking back, I realize that belonging is rarely something you feel immediately. More often, it is something you build through persistence, one challenge at a time.

Becoming the Leader I Needed

As my responsibilities grew, I began to understand that success in engineering and cybersecurity is not only about technical expertise. It is also about leadership, communication, collaboration, and trust.

I often think about the young woman who walked into her first security meetings convinced she was the least qualified person in the room. If I could go back and tell her one thing, it would be this: you do not have to know everything to belong here.

Today, one of the most rewarding parts of my career is helping others find their place in technology. I mentor students, support women entering cybersecurity, and work to create opportunities for the next generation. I do it because I remember exactly what it felt like to question whether I belonged.

The Lesson I Hope Every Woman Carries Forward

If there is one lesson my journey has taught me, it is that you do not need a perfect plan to build an extraordinary career.

The opportunities that change your life rarely arrive in the form you expect. Sometimes they look like a city you decide to move to. Sometimes they look like a role you almost did not apply for. Sometimes they look like a field you never planned to enter.

The young girl learning about computers from a blackboard could never have imagined becoming a cybersecurity leader. Yet every chapter of the journey mattered. The uncertainty mattered. The career pivots mattered. The moments of self-doubt mattered.

On this Women in Engineering Day, my hope is that more women recognize that there is no single path into engineering or technology. Curiosity matters. Resilience matters. The courage to keep learning matters.

The engineer I became was never part of the plan.

And she turned out to be exactly who I was meant to become.

What does success look like when you’re no longer willing to trade everything for it? I spent the past year figuring that out.

After more than 30 years in big tech, I took my first real professional break. Time away gave me something I hadn’t had in decades: space to reset my professional priorities. I had the rare chance to look at my career from the outside in. I saw the toll that constant travel, relentless pace, and “always on” expectations took on my health, my family, and my sense of joy. I heard similar stories from other women at this stage of life, accomplished leaders quietly questioning how they want work to fit into their 50s and beyond. That reflection clarified what I want more of, and what I’m no longer willing to trade away.

Now I’m back, and I’ve rewritten my playbook. As I settle into my new roles as a TechArena co-founding Advisor and a board member at Jabil (NYSE: JBL) and Dycom Industries (NYSE: DY), I’m using four principles as my blueprint to redefine ambition, leadership, and impact in this next season of my career.

1. Outward, Not Upward

My old motto for evaluating new opportunities was: “If I’m equal parts excited and terrified, I’m in the right place.” That mindset pushed me to take on more, seek bigger roles, move up. It’s a good mindset for climbing the ladder.

Today, upward is no longer my goal. It’s outward. I want to learn about new technologies, experience new industries, work alongside people who genuinely inspire me. Fancy titles, large salaries, and big organizations offer perks and prestige, but they require significant sacrifices, too, and I’ve gotten clear-eyed about what I’m willing to trade.

Success now means surrounding myself with people I admire and trust, building collegial relationships, and letting curiosity pull me forward instead of fear or competition pushing me from behind.

2. People Over Span of Control

I miss the days of truly knowing my team. When you’re managing thousands of people, the organization becomes distant. You lose names. You lose faces. I thrive on personal relationships and a leadership style that prioritizes developing the individual along with delivering results.

This isn’t about only working with small companies. Jabil and Dycom are anything but small. It’s about choosing roles where I have a fighting chance to know the people I work with, personally and professionally. There is value in intimacy in leadership.

The same principle drives how I want to operate inside organizations: with quick, clear decision-making and less bureaucracy. So many companies talk about moving fast, making mistakes, learning, and course correcting; few have the tolerance when it comes down to it. I want to empower innovation through genuine, empathetic support: identify decision-makers up front, keep them to a minimum, and push decisions down the leadership stack whenever possible. Learn together, try again quickly, give people time and grace to take risks.

Patience, applied at scale, is its own kind of leverage.

3. The View from the Boardroom

I got my first taste of board service in 2022, when I joined the Board of Directors at Lattice Semiconductor. I stepped down in 2024 in accordance with my role at AWS, but the experience reframed how I think about contribution. Going deeper into a company as an advisor or director has been more rewarding than I expected.

The role of an advisor or director is fundamentally different from operating, and that difference is the point. Operators live inside their company’s day-to-day. Boards and advisors get the rarer privilege of seeing patterns across companies, asking the questions executives are too close to ask, and helping decide which bets are worth making. After 30 years on the operator side, I have a lot of pattern to draw from, and I’m finding I’m just as happy being the right hand to a CEO I admire as I am leading a business of my own. That balance has become essential to my career satisfaction, and I believe it makes me a better, more insightful executive in any seat.

This is why I joined TechArena Advisory as a co-founding Advisor, and why I said yes to Jabil and Dycom. Each is a company whose mission, leadership, and trajectory I genuinely admire, and each gives me the chance to contribute at the level where strategy is set.

4. Plan in Parallel

I’m a big believer in exploring new opportunities every year. Interview internally or externally to keep your network, interviewing skills, and resume fresh. Explore new industries, expand your skills, and add to your network of experts and mentors. Cultivate areas for future growth.

A mentor of mine taught me that I should look for a new role every three to four years. The first two years, you are ramping to performance and autonomy. The third year, you are mastering the work. By the fourth or fifth year, your company is getting the benefit of your mastery, but you may find your upward—or outward—development has slowed. By planning three to five years ahead and exploring options, you’ll have many more opportunities waiting when it’s time to move, planned or unplanned.

The reason I had options when I needed a break is that I’d been quietly building them for 30 years.

One Last Thing

At the end of each work week, I always took a mental check: had I moved forward, backward, or stayed in place? If I hadn’t made real progress in a few months, I committed to creating a change. Stagnation will kill innovation, in business and in life.

The harder question is how to make that change. For me, it comes down to setting boundaries and being willing to accept the trade-offs that follow. I put my career on pause when my twins were born. I did it again last year. I was fortunate to be able to do so, and the trade-offs were real both times.

If you’re quietly asking yourself these same questions, know that you’re not alone. I’d love to hear your thoughts on sabbaticals or career shifts. What lessons have guided your own professional journey? Has your perspective changed in your 50s?

Best,

Raejeanne — together, we win.

HPE's Trish Damkroger joins Allyson Klein to talk exascale, sovereign AI, 400kW racks, and liquid cooling shaping the next era of supercomputing.

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.

For decades, every sensor in a vehicle has needed its own brain. Ultra Ethernet ends that requirement. In my last piece, I argued that Ultra Ethernet is a far more optimal networking layer for the Software-Defined Vehicle (SDV) than TSN, pointing to bandwidth, congestion management, and the shift from managed scarcity to abundant capacity. There is a deeper implication I want to take up here. Ultra Ethernet does not just move more data faster. Its flat, fabric-style topology changes where intelligence needs to sit in the vehicle, and that change lets architects rethink the sensor layer itself.

The Sensor Tax Has Been Normalized

For decades, the industry has operated on an unspoken assumption that every sensor needs a processor at the edge. A camera needs an ISP. A lidar needs a pre-processor. A radar needs a DSP. These smart sensors were not a choice. They were a necessity born from the fact that the network between edge and core could not carry raw data at the scale modern algorithms demand.

TSN Ethernet reinforced this model. With 1 Gbps links and statically scheduled traffic, the only way to make the math work was to pre-process, compress, and filter the data at the sensor. The camera sends a reduced stream, metadata, or object list, not raw pixels. The lidar runs first-pass segmentation locally. The radar extracts tracks and sends a sparse list.

This is the sensor tax. Every smart sensor adds cost, power, heat, and failure modes at the edge. It also locks the vehicle into a specific processing pipeline. The algorithm on that camera ISP is typically baked in by the supplier. If the OEM wants to change the perception stack, the conversation starts with a hardware redesign, not a software update.

What a Flat Fabric Actually Means

Ultra Ethernet is not a faster bus with a more capable scheduler. It is a fabric. In a data center, that means any endpoint can reach any other with predictable latency, and the network handles congestion rather than pushing that problem to the application layer.

With Ultra Ethernet, the network between a camera and the central compute cluster is no longer a narrow pipe that needs rationing. It is a wide, adaptive fabric that carries raw sensor data from dozens of endpoints simultaneously without collapsing. Packet spraying, end-to-end flow control, and congestion signaling mean the network behaves like a shared resource rather than a collection of point-to-point contracts.

That changes the design equation. If the network can carry raw data, the sensor does not need to be smart. It can be a photodiode array with a serializer, a lidar receiver that just ships point clouds, or a radar frontend streaming raw ADC samples. The intelligence moves from the edge to the core, where it belongs in a compute-centric architecture.

The Economics Are Not Subtle

While the average selling price of today’s vehicle is well in the range of tenss to hundreds  of thousands of dollars, the cost of every single component, be it wiring, semiconductors, or sensors, is scrutinized down to the last cent. A smart camera module with integrated ISP can cost three to five times what a raw imager with a serializer costs. Multiply that across a dozen cameras, several lidar units, and radar modules, and the savings are platform-level economics.

But the real savings are not just BOM. They are in the flexibility that comes from decoupling the sensor from the algorithm. If the perception stack runs on a centralized AI computing cluster, the OEM can update it over the air, swap in a new neural network, change fusion weights, or experiment with different sensor combinations without redesigning hardware at the edge.

This is what software-defined actually looks like. Not just over-the-air (OTA) updates to the infotainment system, but the ability to retrain the entire perception pipeline because the raw data is available in the core, not locked behind a pre-processing layer defined years ago by a Tier 1 supplier.

The Safety Argument

There is a reflexive objection which is that smart sensors are safer because they reduce dependency on the network. If the link goes down, the edge processor still functions. That is true, but it is a design choice, not a law of physics.

A flat Ultra Ethernet fabric can logically be designed with redundancy. Multiple paths, adaptive routing, and congestion-aware forwarding mean the network is more resilient than a static TSN topology where a single link failure breaks a deterministic schedule. The question is not whether you trust the network. It is whether you trust a network designed for the 1990s or one designed for the 2020s.

Moreover, the safety argument cuts both ways. A smart sensor with local processing is a single point of failure with its own software stack, thermal profile, and supply chain. A dumb sensor with a serializer is simpler, with fewer failure modes. The complexity moves to the core, where it can be monitored, redundant, and updated.  As an aside, it also simplifies the challenges associated with managing security.

The Supplier Dynamic

This shift will disrupt the supply chain. Tier 1 suppliers have built business models around smart sensors. The camera module with integrated ISP and object detection is a product line, not just a component. Moving to raw sensors changes the value proposition.

But OEMs are already moving in this direction. They want to own the algorithm stack. They want to train their own models. They want the flexibility to source raw imagers from one supplier and run perception on silicon from another. The flat fabric that Ultra Ethernet enables is the technical foundation for that business model.

The suppliers who adapt will ship high-quality raw sensors with robust serialization and minimal local processing. The ones who cling to the smart sensor model will find themselves competing in a shrinking market as the compute-centric architecture becomes the default.

What This Means for 2028 and Beyond

The 2028 to 2029 platforms I referenced in my last piece are already making these decisions. The teams designing those architectures are not asking whether Ultra Ethernet can carry raw camera data. They are asking how many dumb sensors they can hang off a single fabric segment before adding a switch.

The answer, based on the bandwidth math, is a lot more than TSN ever allowed. A single 100 Gbps Ultra Ethernet segment can carry the raw output from dozens of 8MP cameras at 30fps, handle multiple lidar point clouds, and stream raw radar data from every corner of the vehicle simultaneously. The fabric scales with the sensor count, rather than forcing a hard trade-off between resolution and edge intelligence.

The Bottom Line

The shift to Ultra Ethernet is not just about faster links. It is about a fundamentally different network topology that enables a fundamentally different sensor architecture. When the network is a flat, adaptive fabric rather than a collection of scheduled pipes, the edge does not need to be smart. The intelligence can live where it is most useful: in the centralized compute cluster, where it can be updated, retrained, and redeployed without touching a single sensor module.

TSN forced us to pre-process at the edge because the network could not handle the load. Ultra Ethernet removes that constraint. The question for architects is no longer how to make the sensor smarter. It is how to make the sensor as simple as possible while letting the fabric do what it was designed to do: move data, in bulk, with predictability, from anywhere to anywhere.

The vehicles that win in the next decade will be the ones whose sensor layers are lean, whose compute is centralized, and whose network fabrics do not artificially force intelligence to the edge just because the pipe was too narrow.  The adoption of Ultra Ethernet in SDVs will be essential in enabling this shift.

A record-diverse round of MLCommons results signals an industry settling on what to train, then splintering over how and where to train it.

MLPerf Training working group co-chair Shriya Rishab sees the field converging on a shared set of best practices for building models. At the same time, she sees the frameworks, silicon, and systems running those models pulling apart into something far more varied than a year ago.

The numbers behind MLPerf Training v6.0, released yesterday by MLCommons, make that tension concrete. The round logged 95 unique systems built on 13 different accelerators and 19 host processors. Sixty percent ran across multiple nodes. Two years ago, in the v4.0 round, multi-node systems were closer to a third of submissions. The default shape of an AI training system has changed, and it now mirrors how real data centers get built.

That breadth is the story this round, more than any single winning time. MLPerf has often been read as an NVIDIA scoreboard. This version reads as a map of genuine plurality: 229 performance results, roughly 1.2 times the prior round, from 24 submitting organizations. Four submitted for the first time, among them Vultr and Korea's TTA. "There are more ways of getting your AI training than ever before," said working group co-chair Pavan Yalamanchili.

The Cloud Moves to the Center

The sharpest shift sits in where training happens. Cloud systems more than doubled against the v5.1 round roughly six months earlier. The independent GPU clouds turned up in force, with CoreWeave, Lambda, Nebius, and Vultr submitting alongside hyperscalers Google, Azure, and Oracle. On-premises build-out has not slowed. What changed is that cloud-hosted training now stands as a credible path rather than a fallback.

Lambda offers one window into the pattern. Its bare-metal GB300 NVL72 run trained Llama 3.1 8B to target 18.7 percent faster than its previous best, 11.59 minutes against 14.25, and it posted an early result on the new GPT-OSS-20B workload using a single eight-GPU node. The takeaway is not the specific time. It is that a cloud provider tracked the newest hardware and a brand-new workload in the same round it shipped.

Providers including Nebius and ScitiX go further, arguing their virtualized or standardized environments now perform close to bare metal. That is a claim worth testing rather than taking on faith, and the benchmark is built to let buyers do exactly that.

Convergence on Sparse, Divergence on Precision

Look at the models, and the industry agrees. Two new benchmarks entered the suite this round, DeepSeek V3 at 671 billion parameters with 37 billion active per token, and GPT-OSS 20B at 21 billion parameters with 3.6 billion active. Both use a Mixture-of-Experts design, which routes each token to a small subset of specialized sub-networks so a large model activates only a fraction of itself per token. The two drew about 22 percent of submissions on debut. Sparse computation is now the shared architecture, and MLCommons is retiring its older dense models, with Llama 3.1 405B and the DLRM-DCNv2 recommender appearing for the last time.

Look at the math underneath, and the agreement dissolves. Submitters reached for competing four-bit precision recipes, NVIDIA's proprietary NVFP4 and the open MXFP4 standard, mostly in the dense linear layers of their runs and not yet in the MoE models. Yalamanchili called the spread of FP4 implementations "not surprising," a sign of an industry still exploring what works. Convergence on the model, divergence on the precision. That split is the technical signature of an efficiency race that has not settled.

New Entrants, More Transparency

Two smaller developments point to where the next rounds go. KRAI benchmarked Isambard AI, the UK's National AI Research Resource, in what it believes is the first sovereign infrastructure to appear in MLPerf. National compute programs now want public, comparable numbers too. And MLCommons began disclosing the precision and parallelism behind each result, optional this round and mandatory later, so buyers can read past a single headline figure to the choices that produced it.

The models are consolidating. The infrastructure beneath them keeps multiplying. For anyone buying or building AI training capacity, that turns MLPerf from a ranking into a map, with more accelerators, more clouds, and more precision recipes, each a real choice carrying real tradeoffs. The question for the next round is which of these roads widen and which quietly close.

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.

Rakesh Awasthi explains how his company uses AI to decode legacy systems, recover lost knowledge, and modernize mission-critical data infrastructure.

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.

Bots. Apps. Agents. Copilots. More “game-changing” demos than anyone can reasonably process. And yet, I still hear the same thing from enterprise leaders:

“Our teams are still moving too slowly.”

This is the thing AI has not magically fixed.The next advantage will not come from generating more content or deploying yet another chatbot. It will come from turning knowledge into behavior faster than your competition.

I am convinced the real advantage will not come from who adopts the most tools first: It will come from how quickly an organization can help its people continuously adapt through behavioral change.

That is the part of the AI conversation that is underdeveloped. We must talk about the human operating system inside the enterprise: how people learn, how behavior changes, how managers reinforce new ways of working, and what it takes for knowledge to become execution.

The Enterprise Already Has More Knowledge Than It Can Use

Most large companies are buried in expertise and data. Product decks, training portals, internal wikis, meeting notes, pivot tables of data and tribal knowledge all pile up across the organization. The issue is not whether knowledge exists, it’s whether it reaches the right person, with the right context, at the right moment, in a way that is useful to them.  

I saw this firsthand in large-scale enterprise environments. Teams would spend months preparing for a product launch. Leaders would travel globally while sales teams would attend multiple trainings. Content would be published and everyone would check the box. Then months later, in front of a real customer, at a critical moment, the knowledge often was not there when it mattered, resulting in a missed opportunity.

AI Has Made the Learning Problem Harder, Not Easier

AI increases the rate of change and pressure, with products and positioning evolving faster and customer expectations and needs changing even more rapidly. Enterprises are being asked to absorb an insane amount of information and apply it with less time, creating friction inside organizations and channels.

The issue becomes whether sellers can apply the right knowledge at the right moment in a month-long sales cycle. The AI skills gap is both a skills and adaptability gap, and it may be the biggest execution risk most companies are underestimating.

Behavior Change is the New Moat

Can your organization turn its knowledge into changed behavior?

Can your partner ecosystem stay aligned when products, positioning, and market conditions change overnight?

This is where enterprise learning has to evolve to become an adaptive system built with humans in the loop, that understands role, context, timing, readiness, and application. Access to information is not the same as readiness. A chatbot can retrieve an answer. It does not automatically build judgment, fluency, confidence, or accountability.  More importantly, it does not guarantee that a team changes how it sells to, supports or serves customers.

The real challenge is helping people perform better in moments that matter, with useful, AI-powered solutions. That is why the next moat will be workforce adaptability. The winners will be the ones that convert knowledge into changed behavior before their competitors even finish rolling out the next tool.

Quantum computing is a transformative technology that is consistently positioned not as a replacement to classical computing, but as complement. As the technology moves out of the lab, the pressing question for technology decision makers is becoming how soon that addition will become relevant to infrastructure build-outs. Solidigm’s Jeniece Wnorowski and I recently sat down with Pranav Gokhale, CTO and co-founder of Infleqtion, for a conversation that explored where quantum fits in the modern compute stack, where it is already delivering value, and what the next few years are likely to bring.

Not a Replacement, a Co-Processor

Pranav started our conversation by exploring the interconnected relationship between quantum and classical computing. Infleqtion sees a future of hybrid quantum-classical computing systems, and supporting evidence for that is already visible in the efforts of the industry’s biggest players.

“GPU has not replaced CPU. It’s been a co-processor,” he explained. “In the same way, we think that CPU and GPU are going to be co-processors to QPUs, or quantum processing units.”

Industry leaders are in agreement. At NVIDIA’s GTC conference, in fact, Infleqtion’s quantum machine was featured at the NVIDIA booth, connected to graphics processing units via NVQLink. The vision is a hybrid compute fabric where software orchestrates workloads across all three processor types, routing the most computationally demanding problems to the QPU while CPU and GPU handle the rest.

Real-World Value, Available Now

Building on this concrete example, Pranav was direct about where quantum-derived value is already being delivered, and the industries span defense, genomics, and materials science.

Infleqtion has deployed quantum-inspired machine learning models to NVIDIA Jetson edge GPUs, and the US Navy and US Army are among its customers. One application is sensor data fusion in environments where GPS signals are being disrupted. That means those in unfamiliar territory could rely on edge-deployed systems that use computer vision or even celestial navigation to maintain positioning.

The genomics application is equally striking. The human genome contains 6 billion base pairs, roughly 6,000 times longer than what current large language models can process in a single context window. Scaling up classical compute does not solve the problem efficiently. “Every time you double the context window, you have to 4X the GPU,” Pranav explained. Infleqtion’s quantum-inspired contextual machine learning model last year set a new record on genomic sequence processing by treating memory constraints differently than classical approaches.

Finally, a joint publication with NVIDIA, released approximately five months before our conversation, demonstrated quantum-GPU co-processing applied to materials science. The target application: understanding how electrons interact inside battery chemistry, a problem with significant commercial implications for improved battery performance and life if it can be solved at scale.

AI and Quantum: A Mutual Dependency

While AI and quantum computing are often discussed as separate tracks, Pranav made clear they are increasingly interdependent.

AI, specifically GPU-accelerated inference, is central to quantum error correction. As a quantum computer runs, it accumulates errors from environmental noise. GPUs can analyze that output, identify where errors occurred, and correct them, much the same way that wireless protocols clean up noise to deliver a clear signal to your phone. “We’re taking noisy quantum bits, qubits, and turning them into a very pristine signal using AI to detect where did something potentially go wrong,” Pranav said.

This is precisely why NVIDIA’s investment in quantum adjacency is deepening. Their GPUs are not just powerful compute resources for classical workloads; they are a critical component of making fault-tolerant quantum computing viable on a faster timeline.

The 2028 Horizon

Infleqtion’s internal roadmap targets 2028 as the year the company expects to reach 100 reliable, fault-tolerant logical qubits. At that threshold, Pranav believes quantum systems will begin outperforming the world’s largest supercomputers on specific, high-value problem classes: materials discovery, drug design, chemistry simulation, and certain AI workloads.

“Every time we make a little bit of progress, it doubles and quadruples and 10Xs the performance of the quantum computer,” he said, describing the non-linear scaling dynamics that distinguish quantum from incremental classical improvements.

The TechArena Take

Quantum computing has spent years as a technology of perpetual promise. What the conversation with Pranav reflects is a field that is transitioning from research curiosity to engineering roadmap. The hybrid CPU-GPU-QPU stack is already being demonstrated. So while quantum purchasing decisions are not quite imminent, the infrastructure decisions made over the next two to three years should account for a compute landscape that soon is likely to look meaningfully different.

Learn more about Infleqtion and its quantum computing and sensing technologies by watching our full podcast or visiting infleqtion.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.

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