Empowering Innovation
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Data Insights podcast with Infleqtion CTO Pranav Gokhale on combining quantum, GPUs, and CPUs to deliver fault-tolerant systems and real-world impact.

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

Four Problems, One Goal

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

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

Where AI Works, and Where It Doesn’t

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

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

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

Regulatory Reality

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

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

The Data Foundation Problem

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

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

The TechArena Take

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

A conversation with qBraid’s Kanav Setia on quantum computing’s evolution, developer challenges, and the role of software in shaping its future.

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Submer's Gabriel Lazar joins TechArena and Solidigm at OCP EMEA on data center sustainability, heat reuse, and lower-impact AI infrastructure.

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

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

Ten Times More Expensive to Move Than to Compute

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

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

Agentic AI Compounds the Problem

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

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

Prefill vs Decode Stage

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

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

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

Future Innovation in Memory  

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

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

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

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

The TechArena Take

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

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

Watch the podcast here.

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

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

A Hybrid Future

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

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

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

Lowering the Barrier with Logical Qubits

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

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

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

Closing the Latency Gap With NVIDIA

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

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

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

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

The Path to Scale

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

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

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

The TechArena Take

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

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

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

Back in 2015, the “godfather of AI” Geoffrey Hinton made a bold prediction: stop training radiologists immediately, because deep learning would render them obsolete within five years. A decade on, this looks unlikely to happen any time soon, and radiologists remain in just as much demand, showing how important accuracy and safety remain and the unique challenges in adopting AI in this space.

My recent conversation with Tapan Shah, AI Architect at Innovaccer and Agentic AI Work Group Lead at the Coalition for Health AI (CHAI), and our Data Insights co-host Jeniece Wnorowski from Solidigm, shed light on some of the challenges in creating scalable AI systems for healthcare. His role involves creating AI systems and agents that work in actual healthcare environments and enterprise systems that affect patient and provider outcomes.

In Tapan’s view, the hardest problem in healthcare AI is not creating the right models or algorithms, but in designing from the ground up.

The Gap Between Pilot and Production

Tapan opened with an example that cuts to the heart of the challenge. An AI clinical note generator built for a cardiology practice may work great in a pilot and then stumble when deployed for other disciplines like oncology or orthopedics, or even a different practice running a different electronic health record (EHR) system. Even when the underlying model remains the same, the results can be vastly different based on the medical discipline.

“Scaling AI into enterprise healthcare is less of an AI problem and more of a system design problem,” Tapan said. “The real problem here is whether in real-world situations, an AI agent being developed has the right level of access and the capability to create sufficiently transparent and explainable recommendations that even a skeptical clinician can accept.”

From Building Models to Agents

In the past decade, the healthcare AI industry has undergone a seismic shift from building predictive models to building agents. Historically, validating an AI system was relatively straightforward: train a model, measure accuracy on a holdout set, and deploy. This has been successfully validated in cases like early tumor detection, says Tapan.

Agents are a fundamentally different beast. They pull data from multiple data sources, invoke various tools, and combine these inputs to perform complex tasks. Often there is no single source of truth and clinicians can interpret the same data differently. Data can be missing or certain users cannot access certain tools or software. In this scenario, the challenge becomes ensuring that the agent being built is safe and can handle the scenario safely and predictably even in a novel scenario.

And because sensitive data is being handled, safeguards need to be built in the system from the get-go. For instance, a cardiology clinical note generator should not have access to a patient’s psychiatric records.

Governance as Enablement, not Constraint

When the topic turned to governance, Tapan pushed back against the assumption that governance is primarily about controls and restrictions.  

“AI governance is not a constraint, it’s enablement,” he said, comparing a good governance framework to a constitution: it can be used as a binding document, or it can serve as the foundation for doing genuinely useful things, based on how you build and use it.

He illustrated this with a scenario where an authorization agent shifted from a 70% auto-approval rate to a 90% auto-approval rate. Effective governance would mean detecting this shift, reviewing the agent’s complete decision graph and identifying the root cause. A successful governance model would enable such decision making to be made in minutes, rather than weeks.

Clinical Consequences

The thorniest issue in the conversation was accountability, especially as AI agents take on decisions with both clinical and administrative consequences. Tapan was candid: there is no perfect solution yet. Legal frameworks are still catching up to the question of what it means for an AI agent to make a consequential decision.

Innovaccer’s current approach is to make sure that there is comprehensive logging of every AI decision, granular access control for agents, and human oversight with the ability to override. For all clinical use cases, and many administrative ones, a human remains in the loop, able to review and reverse any AI-generated decision. As legal and governance frameworks evolve, these foundations will provide the structure to adapt.

Long-term Value  

When asked about measuring long-term strategic value, Tapan pointed to two holy grails: improved patient and provider outcomes. Treatment authorizations are a good example of where AI intervention can help, he explained.  

“There are cases where it can take upwards of two to three weeks for a prior authorization for a procedure, that leads to delay in care,” he said. “If we can bring that down to, let’s say, a day, less than a day, even a few minutes, it actually impacts patient outcomes and cost of care.”  

On the other end, freeing clinicians of administrative burdens allows them to spend more of their time caring for patients, reducing burnout and stress levels.

And because healthcare AI serves multiple stakeholders including operations, compliance and clinical teams, a scalable solution would need to be designed with solid system design principles, with observability, tracing, and monitoring built in right from the very beginning.

The TechArena Take

Innovaccer’s approach demonstrates the challenges in building a successful system that can work across multiple specialties in real-life hospital scenarios. As integrating AI in healthcare has shifted from building models to building agents, the hardest problem to solve isn’t technical performance, but rather ensuring safety, accountability, and governance.  

Tapan’s framing that governance should be treated as enablement, not constraint, feels like an important mindset shift for leaders trying to move beyond the pilot stage. By helping to reduce authorization times and administrative burden, AI can help provide long-term benefits such as better patient care and provider experience.

If you’re interested in learning more, check out the full podcast. In addition, the Department of Health and Human Services recently published updated guidelines for AI, and the CHAI and Innovaccer websites provide useful guidance on the use of agentic AI use in healthcare settings.

The moments that help define an employee's trajectory, including performance reviews and manager feedback, are too consequential to get wrong. AI promises to help managers be better prepared for these important conversations by presenting clear insights that draw from the sea of daily work data. But it can only deliver when it is trusted on all sides.

In my recent conversation with Maher Hanafi, senior vice president of engineering at Betterworks, and Solidigm’s Jeniece Wnorowski, we discussed what it takes to turn AI’s potential into a trusted and valued enterprise solution.

Beyond the Static HR Database

Betterworks describes itself as a talent and performance management platform for global enterprise customers, but Maher is quick to distinguish it from traditional HR software. Where legacy tools function as administrative record-keepers by tracking history, storing documents, and managing lists, Betterworks aims to orient its platform around the flow of work.

“We were looking at the data from a performance lens,” Maher explained. “We’re trying to enable anything that helps go beyond just tracking history…to focus more on the flow of work.” For large enterprises with complex organizational structures spanning multiple regions, that means helping individuals, managers, and business units connect their daily efforts to company-wide goals, a capability that only becomes more valuable, and more technically demanding, as AI matures.

AI as Horizontal Intelligence

Maher offered the useful frame of thinking about AI as enabling “horizontal intelligence.” Before AI, Betterworks’ modules — goals, feedback, one-on-one meetings, talent and skills — operated as largely separate domains. Generative AI has made it possible to interconnect those domains in ways that weren’t previously practical.

“With AI today, it’s just way easier to interconnect all of these,” he said. “I think SaaS products and SaaS platforms will be built as more of an interconnected set of layers that will break the silos between different components and features.”

In practical terms, this means a manager preparing for a one-on-one meeting can receive and review AI-generated insights drawn from an employee’s recent goals, feedback, and performance history before a conversation, rather than manually pulling together and examining months’ worth of data.

Responsible AI as a Design Principle

When AI provides insights that can influence such important conversations, it’s paramount that all parties can trust the system’s output. Operating in this environment, Betterworks has emphasized responsible AI guided by two principles in particular: transparency and explainability. Transparency means the system can show users what sources it drew on to generate a response. Explainability means users understand why an AI suggestion is what it is. With this foundation, when managers are giving feedback to employees based on information AI provides, they can make suggestions and have confidence in the underlying insights.

“We are trying to use AI as a way to really get you as a better individual, better member of the organization and contributing to the big picture versus having AI take control,” Maher said. “You should be in the driver’s seat. AI is just there to help you and be a co-pilot, nothing else.”

Advice for Technology Leaders

As the conversation turned to broader lessons, Maher offered practical guidance for engineering and technology leaders navigating AI adoption inside enterprise organizations.

His first recommendation is simply to stay informed without becoming overwhelmed. “AI is moving very fast…. Picking the one out of the haystack is very challenging,” he said. To manage that, he created what he calls an AI Engineering Lab at Betterworks, a structured environment where engineers could explore tools and run experiments, rather than waiting for top-down mandates on which technology to adopt.

He also urged leaders to take the financial dimension seriously. “There was a huge risk of AI taking too much money without achieving ROI,” he said. “Turning into someone who cares more about the financial aspect and looking at costs on a frequent basis…was a huge success.” In his view, senior technology leaders increasingly need to think with some of the rigor of a chief financial officer when it comes to managing AI infrastructure spend.

Finally, he pointed to the value of frameworks. His own AI maturity framework and a flywheel model focused on planning, building, and optimizing AI systems have helped keep the team oriented even as the technology underneath them continues to shift.

The TechArena Take

Maher’s perspective reflects a measured but substantive view of what AI can deliver in enterprise software, one grounded in the realities of compliance-heavy industries and the organizational complexity of global customers. Rather than positioning AI as a transformation layer bolted onto an existing product, Betterworks has committed to rebuilding the platform’s foundations to make intelligence a native capability. For technology decision makers evaluating AI-powered SaaS in regulated environments, the Betterworks story offers a useful model.

Learn more about Betterworks at Betterworks.com, and watch our full podcast episode.

ZeroPoint’s Nilesh Shah explores why data movement, compression, and memory bandwidth now shape AI inference performance, and where heterogeneous systems and quantum may fit next.

AI architect Tapan Shah joins the Data Insights podcast to discuss scaling AI in healthcare, governance, AI agents, and how technology can improve patient outcomes and reduce provider burnout.

Maher Hanafi of Betterworks joins TechArena Data Insights to discuss AI in enterprise SaaS, why many AI proof-of-concepts fail, and how engineering leaders can successfully move AI into production.

As organizations build private AI clouds to control costs and protect their data, they face a familiar dilemma: the trade-off between performance and operational simplicity. Hyperscalers (like AWS or Google) have both, but only because they have armies of engineers to build custom software that tames their hardware.

My recent conversation with Solidigm's Jeniece Wnorowski and Marc Austin, CEO and co-founder of Hedgehog, revealed how enterprises can now access that same "Hyperscaler Agility"—without the army of engineers.

The key? Decoupling the control plane from the hardware.

The Hyperscaler Playbook for Everyone

Hedgehog’s mission centers on enabling enterprises, government agencies, and neoclouds to “network like a hyperscaler.” This means moving beyond rigid trade-offs. Instead of being forced to choose between the stability of validated reference architectures or the flexibility of open standards, Hedgehog allows organizations to leverage both, orchestrated by a single software platform.

This approach offers a massive strategic advantage: Supply Chain Resilience.

As Marc explained, a diversified hardware strategy is critical for risk management. “If you have a supply shock—like a global pandemic or a trade war—that can limit your ability to scale because supply becomes constrained,” he noted. “You can’t add capacity to your network when you need to.”

By running open-source software on OCP standards-based servers, organizations can acquire equipment from whichever vendor offers the best price and availability at that moment. And because Hedgehog’s control plane is hardware-agnostic, it can eventually extend this same flexibility to other high-performance reference architectures, ensuring that the software experience remains consistent regardless of the underlying silicon.

Automating the "Network Appliance"

Hardware diversity is only half the battle; the other half is operational speed. Hedgehog delivers all the software needed to automatically install, configure, and operate AI networks as a turnkey "appliance." This eliminates weeks of manual configuration work by network architects.

More importantly, it democratizes access. By providing a Virtual Private Cloud (VPC) service, Hedgehog allows enterprise users or neocloud tenants to operate within a private, secure segment—consuming on-premise AI infrastructure with the same self-service ease they expect from a public cloud provider.

Real-World Agility: Zipline & FarmGPU

The power of this "Universal Control Plane" is evident in how customers are using it to bypass traditional infrastructure bottlenecks.

Zipline, an automated drone delivery company, utilized Hedgehog to build a private cloud that cut infrastructure costs by 70% while keeping their delivery data secure. The critical win wasn't just the hardware savings—it was the operational model. They managed the deployment with their existing DevOps team, without hiring specialized network engineers, because Hedgehog abstracted the physical switching complexity into simple software commands.

In the high-performance arena, FarmGPU (operating the Solidigm AI Central Lab) used Hedgehog to orchestrate an 800G fabric for AI training. Independent testing by SemiAnalysis highlighted that Hedgehog’s software-defined congestion management maximized bandwidth and GPU utilization.

This proves a vital point for the future of AI: The software you use to manage the network matters just as much as the wire itself.

Solving the "Gateway Problem"

Agility isn't just about the switch fabric; it's about how data enters the building. FarmGPU faced a challenge familiar to many AI operators: ingesting terabytes of training data through a limited enterprise firewall.

Legacy solutions required expensive, proprietary hardware routers. Hedgehog’s software-defined gateway turns standard x86 servers into high-performance routers. This effectively brings the functionality of a public cloud "Transit Gateway" on-premise, allowing secure, multi-tenant segmentation for AI workloads.

The TechArena Take

Hedgehog is redefining the role of the network in the AI stack. By focusing on a hardware-agnostic control plane, they are ensuring that the "Brain" of the network (the automation) is distinct from the "Body" (the switch).

This is the architecture of the future. It gives enterprises the ultimate luxury: Choice. It allows IT leaders to select the best hardware for their specific workload—optimizing for cost, performance, or supply chain availability—while maintaining a consistent, automated operating experience across the entire fleet.

For organizations that view data as their competitive moat, this ability to unify diverse infrastructure under one automated standard is the key to scaling AI.

In the race for advancing technology, time, funding, and attention are often dedicated to immediately monetizable applications. While industry roadmaps certainly drive technological advancement, basic science, which forwards our fundamental understanding of the universe, can create breakthrough findings with wide-reaching applications and effects.

My recent conversation with Silvia Zorzetti from Fermilab and Solidigm’s Jeniece Wnorowski revealed how such research into the convergence of high-energy physics and quantum technology is creating outstanding developments for quantum computing.

From Colliders to Qubits

As a U.S. particle accelerator laboratory, Fermilab has spent decades perfecting superconducting cavities that accelerate particle beams to near light speed. Through years of study, researchers at the lab have identified several sources of noise that can make these superconducting cavities less efficient, and they have worked to eliminate those sources of loss.

In 2017, researchers began studying these same cavities at the quantum level. As Silvia explained, at this single photon level, the energy is much lower, which means there are new potential sources of loss compared to the higher energy levels. “We can focus on the basic science and the basic understanding of those mechanisms,” Silvia explained.

At the same time, Fermilab is finding ways to transform superconducting cavities to be efficient for quantum computing. By placing qubits inside the cavities, Fermilab has achieved 20 milliseconds of coherence. That coherence time represents a critical advance in the typical rapid decay time of quantum information. “And we know that it is possible to achieve more coherence,” Silvia said.

Quantum Computing’s Sweet Spot

Quantum computers won’t replace classical computers for all tasks. The technology excels at specific problems, including the Shor algorithm for prime number factorization and Grover’s algorithm, a quantum algorithm for unstructured search. As the threats to digital security grow, the Shor algorithm is of extreme interest because of its efficient ability to factorize prime numbers, a process that is very important for cryptography. “This means that we can have systems that are very secure because they cannot be broken by someone else with a more advanced cryptography system, let’s say an alien, because here on Earth, we all have to comply with the quantum mechanics rule,” she explained.

Beyond these practical applications, quantum computing excels for quantum simulations for field theories, which involve many interacting components. “We have these huge many body problems in which there are so many entities. We know how to describe them if they are alone,” Silvia explained. “But then when they start to interact to each other, it becomes a very complex model. So, we know that quantum computing is very good for those kinds of applications.”

Roadmap Challenges

While quantum computing has seen great progress, challenges remain to realizing its full potential. Silvia identified two types of hurdles to be overcome: “engineering problems,” where technical challenges are understood and need to be addressed, and problems where more fundamental research is required.

“Those are mainly between the interconnects,” she said. “The interconnects are needed for scaling quantum computing…and in particular, interconnects with very low losses.” Quantum information is a weak signal, so research to find solutions that will prevent the loss of this information remains a priority.

Quantum error correction presents another major hurdle. Quantum states are inherently fragile, and errors can arise in quantum information due to decoherence and other issues. The community is developing algorithmic techniques that make computations more robust to noise, while simultaneously working on hardware improvements.

The Promise of Quantum Sensing

The environmental sensitivity that creates problems for quantum computing is actually an advantage for quantum sensing, which is already delivering results. Fermilab is leveraging this sensitivity to detect dark matter, dark photons, and axions. It is also studying radiation, like the gamma ray, to understand better how these affect quantum computers, and how quantum computers could be built that are robust to radiation.

The TechArena Take

Fermilab’s approach to quantum computing demonstrates how domain expertise from one field can catalyze breakthrough innovations in another. By leveraging decades of superconducting cavity development for particle accelerators, the laboratory has achieved amazing quantum coherence times. The pursuit of fundamental knowledge about how materials behave at the quantum level has yielded practical breakthroughs that are now accelerating the entire field forward.

For those interested in learning more, Fermilab hosts regular symposiums and outreach programs, including their Quantum 101 track designed for non-experts. Learn more about Fermilab’s quantum research at fnal.gov.

In higher education, information technology infrastructure often operates behind the scenes, quietly enabling learning without drawing attention to itself. For Rose-Hulman Institute of Technology, that philosophy recently drove a significant infrastructure transformation. The goal was straightforward: remove barriers so faculty and students can focus on research, teaching, and learning rather than wrestling with technology limitations.

During my recent TechArena Data Insights episode with Solidigm’s Jeniece Wnorowski and Justin Baker, systems administrator lead at Rose-Hulman, Justin shared how the institution modernized their infrastructure. The results demonstrate how strategic infrastructure investments can dramatically improve operational efficiency while directly supporting educational outcomes.

The Challenge: When Infrastructure Becomes the Bottleneck

Before its latest upgrade, Rose-Hulman’s previous infrastructure challenged system administrators in a variety of ways. Older, disparate systems that were pieced together created slowdowns in trying to do any sort of maintenance, from bringing systems back up if they went down to meeting the demand to roll out new software.

For a small IT team managing everything from student information systems to enterprise resource planning platforms and Microsoft 365 administration, these delays were a serious hindrance. The team needed infrastructure that would let them respond rapidly to emerging needs rather than constantly fighting the limitations of aging hardware.

“Upgrading made the most sense in terms of being able to get that speed and that ease of use….and making fewer points of failure,” Justin explained.

Doing More with Less Through Strategic Partnerships and Modernization

Rose-Hulman’s decision to upgrade by partnering with DataON and incorporating Solidigm solid-state drives (SSDs) as the storage foundation centered on technical compatibility. As a Microsoft shop running primarily Windows servers, Rose-Hulman saw DataON’s close collaboration with Microsoft as a perfect fit. In addition, DataON’s hardware expertise ensured the new infrastructure would support Rose-Hulman’s critical administrative and educational systems.

The performance improvements following the infrastructure upgrade were substantial. Scheduled maintenance windows that previously consumed six to eight hours now are completed in under three hours. Server deployment timelines have been compressed from up to two hours to 10-to-15 minutes. The team no longer needs to wait for “after hours” time blocks to do maintenance or fine tuning, and has time to address critical institutional systems.

“We’re able to run more with less,” Justin explained. “So we can focus on the types of things that allow us to add reliability or backup or something like that to our environment versus having to front-load most of the infrastructure for it just to run everything.”

Piloting a New Model for Access to Engineering Applications

Beyond upgrading core infrastructure, Rose-Hulman is exploring how Azure Local paired with Azure Virtual Desktop (AVD) and NVIDIA L4 graphics processing units (GPUs) can transform software delivery for students. The pilot deployment runs demanding engineering applications through virtual desktop infrastructure, eliminating the traditional constraint of needing powerful local hardware.

This approach addresses a longstanding challenge in engineering education: ensuring every student can access resource-intensive applications regardless of the device they own. By centralizing compute resources and delivering applications virtually, Rose-Hulman can provide consistent performance and eliminate student concerns around having the right high-performance device, or needing to make time to get to a lab to complete coursework.

The TechArena Take

Rose-Hulman’s infrastructure transformation illustrates how strategic technology investments can directly support educational missions in higher education. By partnering with vendors who understand their technology ecosystem and deploying high-performance storage solutions, the institution is achieving measurable operational improvements that cascade into better student experiences. For educational institutions managing tight budgets and small IT teams, efficiency gains translate directly into capacity for innovation and improved service delivery.

As Rose-Hulman continues expanding their Azure Local deployment and virtual desktop capabilities, they’re positioned to offer students greater flexibility and access while maintaining the high-performance infrastructure that engineering education demands. This balance between operational efficiency and educational excellence reflects the thoughtful approach required when infrastructure decisions directly impact student success. Learn more about Rose-Hulman Institute of Technology at www.rose-hulman.edu.

As organizations deploy AI models at scale, a new set of challenges has emerged around operational efficiency, developer velocity, and infrastructure optimization. A recent conversation with Solidigm’s Jeniece Wnorowski and Brennen Smith, head of engineering at Runpod, revealed how cloud platforms are rethinking the entire AI stack to help developers move from concept to production in minutes rather than months.

The Economics of AI Inference

Runpod operates 32 data centers globally, providing graphics processing unit (GPU)-dense compute infrastructure for small companies and enterprises building and deploying AI systems. This service is crucial considering the economics of modern GPUs, where a single system with 8 GPUs can cost hundreds of thousands of dollars. Runpod understands that the compute hardware is only part of the equation. “Storage and networking…glue these systems together,” Brennen said. “By ensuring that there’s high quality storage paired up with these GPUs….we have been able to show that this results in a markedly better experience.”  

On top of this, the company provides a sophisticated software stack that allows developers to go from their idea to production in minutes, across training and inference use cases. The goal is to “Make it so developers and AI researchers can focus on what they do best, which is actually delivering value to their customers,” Brennen said.  

The ability to rely on optimized infrastructure is becoming even more important as organizations move from training to deployment. Smith likened training infrastructure to traditional business capital expenditures, noting that the high up-front costs see a return on investment over a long period of time. In inferencing, organizations deal with ongoing operational realities, grappling with scaling, efficiency, and delivering value to customers daily. As a result, Runpod has engineers specifically looking at inference optimization. With the rise of AI factories, “How well these systems are run from an operational excellence perspective will dictate the winners and losers,” Brennen said. “You run an inefficient factory, you’re out.”

Storage as the Innovation Accelerator

One of the most important insights from our conversation addressed storage, which is now seen as a hidden bottleneck in AI. Brennen recounted how his engineering team recently investigated Docker image loading times. While unrelated to a specific large language model (LLM) activity, developers flag issues like slow loading times as hurting their overall workflow. This gets in the way of things needing “to magically just work.”

For the solution, Brennen reiterated that storage is what glues the system together. “What we have found is every time, as long as we are optimizing our storage, we are able to make the data move faster,” he said. And when data movement is optimized, entire development cycles accelerate.

Runpod recently launched ModelStore, a feature in public beta that leverages NVMe storage and global distribution to make AI models seem to appear “like magic.” What previously took minutes or hours now happens seamlessly, compressing development iteration cycles. For organizations under pressure to deliver AI capabilities quickly, these time savings compound into significant competitive advantages.

Brennen emphasized that faster developer cycles enable teams to fail fast and iterate more effectively to deliver successful outcomes. When CTOs receive mandates to implement AI, their success depends on giving teams tools that accelerate innovation rather than creating additional friction.

The Convergence of Infrastructure and Code

Looking ahead, Brennen identified the convergence of infrastructure and software as a transformative trend. The goal is to enable code to self-declare and automatically establish the infrastructure required to run it, freeing developers from thinking about infrastructure so they can focus on their code and creating value aligned to business logic. “Anything we can do to make it even easier to get global distribution, that’s a hugely powerful paradigm,” he said.

The TechArena Take

Runpod’s emphasis on developer experience demonstrates that sustainable AI deployment requires thinking holistically about the entire infrastructure stack. The company’s focus on making complex infrastructure feel magical to developers reflects a broader industry recognition that reducing friction accelerates innovation.

As AI moves from experimentation to production deployment, organizations that optimize for developer velocity and operational efficiency will have a significant advantage from their ability to accelerate time to value. For organizations evaluating AI infrastructure partners, Runpod’s approach offers a model that balances performance, scalability, and ease of use.

Connect with Brennen Smith on LinkedIn to continue the conversation, or visit Runpod’s website and active Discord community to explore how their platform might support your AI initiatives.

Fermilab’s Silvia Zorzetti explains how quantum computing and sensing are evolving, where they outperform classical systems, and what’s next for the field.