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The next wave of AI won’t live in a data center—it will weld seams, pick bins, and navigate factories alongside people. Physical AI brings intelligence into machines that perceive, decide, and act at the edge, closing the loop between perception and action.

Real-world factories introduce drift, glare, vibration, dust, and unpredictable human behavior—conditions that most models never see in simulation.

For IT and cloud architects, this is a stack problem with hard requirements: real-time inference under adverse conditions, data pipelines that span OT and IT, and operational discipline that turns pilot demos into consistent, reliable uptime. The gap between ‘works in simulation’ and ‘works every day on the line’ remains the main blocker to scale.

But it’s also a workforce problem. Robots reduce injury risk in hazardous tasks, but displacement effects are real and localized. The question isn’t “robots: yes or no?” but “how do we deploy them responsibly with both operational rigor and a workforce plan?”

Why the Market is Ready 

Three curves are converging: cheaper edge compute and sensors, strong perception models, and maturing MLOps for robotics. The International Federation of Robotics reports 542,000 industrial robots installed in 2024—the fourth consecutive year above 500k—with global demand doubling over the past decade. Industrial AI spending is projected to grow from $44 billion in 2024 to $154 billion by 2030. 

Standards are accelerating deployment. ROS 2/DDS, OPC Unified Architecture, and OCP’s rack-level guidance are pushing interoperability across sensors, controllers, and training infrastructure. The blockers are shifting from feasibility to integration discipline and change management - not ‘Can we automate?’ but ‘Can we maintain reliability when conditions vary beyond 5–10% of training data?’

Industry-grade platforms now stitch together simulation, data pipelines, and robotics foundation models. NVIDIA’s Omniverse plus Isaac tools let teams generate synthetic data, train policies in digital twins, and validate behaviors before touching a live cell—shrinking iteration from months to days. The missing piece is capturing the tribal knowledge of veteran technicians and encoding it into recovery behaviors robots can execute.

The Architecture That’s Working

I spoke with Durgesh Srivastava, CTO of DataraAI, at the recent OCP Global Summit about what separates production systems from pilot theater. His outlook is pragmatic: target full automation for bounded task families, match human quality, and build graceful fallbacks when reality goes off-script. 

DataraAI provides a data engine for physical AI—a data-as-a-service platform that transforms factory experience into machine intelligence. It captures how technicians act, how robots fail, and how edge conditions drift, creating the data foundation real-world robotics has always lacked.

The company emphasizes three pillars: 

1. Egocentric Multi-Modal Data Capture – Robots learn from the same viewpoint they act in. DataraAI’s robot-mounted and wearable sensors record RGB-D vision, IMU, tactile, and audio data from real operations. This captures the nuanced cues—force patterns, drift, micro-failures—that static cameras miss.

2. AI-Driven Annotation – DataraAI’s engine automatically labels rare and high-impact events—fires, spills, breakdowns, human-robot handoffs—turning chaos into structured data. It consistently captures scenarios that traditional CV pipelines fail to label or detect.

3. Continuous Learning Loop – New anomalies are fed back into the data engine. Each cycle makes models more resilient and accurate in the field. Every exception becomes new training data, creating a self-improving loop tied directly to real operations.

Early industrial pilots using this loop showed a 53% accuracy lift and 67% better edge-case handling—clear evidence that real-world data closes the performance gap.

The winning pattern is consistent: push perception and control onto the robot, treat the cloud as training and update infrastructure, and run a disciplined data loop that captures real-world anomalies. In harsh conditions—glare, occlusion, spark bursts—edge models must keep the task running. Inference runs locally on the robot, keeping factory data on-site, reducing latency, and enabling real-time adaptation to drift and anomalies. The back end aggregates field data and pushes lightweight updates routinely. That loop turns demos into dependable production.

High-fidelity simulation generates diverse synthetic data and lets teams rehearse rare events safely. Foundation models provide generalizable priors; reinforcement learning in sim refines task skills before transfer to the real world. Edge inference runs locally; telemetry goes upstream for labeling and augmentation; new policies return during maintenance windows. Daily updates are feasible when you structure the pipeline—this reduces drift and grows edge-case coverage.

The Workforce Reality 

Case studies show robots cut musculoskeletal risk and reduce exposure to hazardous tasks like forging and welding. Collaborative-robot safety standards (ISO 10218, ISO/TS 15066) and OSHA guidance formalize safe human-robot interaction.

But displacement effects are real. MIT research found that each additional industrial robot per thousand workers reduced employment and wages in affected commuting zones—a meaningful impact not fully offset by productivity gains. The IMF estimates about 40 percent of jobs worldwide are exposed to AI impact; roughly half may see productivity augmentation, while the rest face reduced labor demand without intervention.

The macro takeaway: adoption will rise, safety can improve, and some roles will shift. For architects presenting to leadership or  works councils, the deployment plan must address both.

TechArena Take 

Physical AI is crossing from pilot theater to production credibility.

The durable advantage comes from operational muscle: how quickly you spin the loop from floor data to better policies and back to the line, while moving people from hazardous repetitive work to higher-value tasks with clear retraining paths. Start with one cell, one exception, and a fallback procedure. Prove you can turn tribal knowledge into machine-executable behaviors and safety risks into measurable improvements. Then scale the loop. That’s the compounding edge in physical AI.

From SC25 in St. Louis, Nebius shares how its neocloud, Token Factory PaaS, and supercomputer-class infrastructure are reshaping AI workloads, enterprise adoption, and efficiency at hyperscale.

Runpod head of engineering Brennen Smith joins a Data Insights episode to unpack GPU-dense clouds, hidden storage bottlenecks, and a “universal orchestrator” for long-running AI agents at scale.

As AI adoption accelerates across industries, financial services takes the front line of both innovation and risk. From fraud detection to customer personalization, AI is reshaping how institutions operate. But the sector’s high stakes and regulatory complexity demand a uniquely careful approach.

At the recent AI Infra Summit in Santa Clara, Jeniece Wnorowski and I sat down with FinTech expert Anusha Nerella for a Data Insights conversation about how financial organizations can responsibly scale AI, stay ahead of fraudsters, and build teams equipped for the future.

“Many institutions are still in the early stages of AI deployment, while bad actors are moving fast and experimenting aggressively,” Nerella said.

This dynamic creates an urgent need for stronger, more agile defenses. Nerella emphasized that financial firms must accelerate their AI implementation cycles without sacrificing the governance and compliance guardrails that define the industry.

Regulatory and Team Culture Shifts

Asked what the broader technology ecosystem should do to support responsible AI in finance and enterprise, Nerella returned to the importance of regulatory alignment.

“Everything has to go through the regulatory and compliance [process] in order to make it responsibly…applicable to the enterprise sector,” she said.

But regulations alone aren’t enough. Nerella believes that financial institutions must rethink team structures and knowledge transfer to keep pace. She advocates for what she calls “reverse training,” in which organizations bring in engineers well-versed in AI frameworks and libraries, then combine their expertise with the strategic experience of senior leaders.

By fostering two-way collaboration between new AI talent and experienced financial professionals, companies can build stronger, future-ready teams.

“It becomes… a collaborative effort for sure,” Nerella explained. “It’s an equal opportunity here because whoever [has] decades of experience…might have limited exposure towards AI-based frameworks or library utilization or hands-on experience.”  

This equal exchange of knowledge, she argued, is essential for success.

Start Small, Stay Governed

For organizations just beginning their AI journeys, Nerella’s advice is both practical and pointed: don’t try to boil the ocean. She recommends starting with “two or three clear use cases with ROI” and ensuring that governance and control mechanisms are in place from the outset.

“When you follow all these basic principles, then you will be able to see…result-oriented AI-based implementation from your end,” she said.

Throughout the conversation, she underscored that AI success in financial services requires human-in-the-loop collaboration.

TechArena Take

The financial sector’s high regulatory stakes, complex legacy systems, and relentless fraud threats make its AI journey distinct. Nerella’s insights highlight that the path forward isn’t just about technology—it’s about culture, compliance, and collaboration.

To build responsible and trusted AI systems, financial organizations must:

• Accelerate adoption cycles without compromising governance.
• Foster reverse training models that marry AI expertise with institutional knowledge.
• Start with targeted, ROI-driven use cases, rather than sprawling transformations.

As the industry races to stay ahead of increasingly sophisticated fraud tactics, success will depend on balancing agility and accountability.

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Recorded at #OCPSummit25, Allyson Klein and Jeniece Wnorowski sit down with Giga Computing’s Chen Lee to unpack GIGAPOD and GPM, DLC/immersion cooling, regional assembly, and the pivot to inference.

The current speed of the data center industry’s transformation is unlike any in its history. Where infrastructure upgrades once followed multiyear cycles, the pace now is annual, at the speed of consumer electronics. My recent conversation with Kelley Mullick, CEO and founder of Avayla, at the Open Compute Project (OCP) Global Summit in San Jose revealed how liquid cooling has moved from niche application to critical infrastructure component, and why standardization will determine whether the industry can meet the moment.

During our TechArena Data Insights episode with Solidigm’s Jeniece Wnorowski, Kelley shared insights from her extensive career in cooling technologies and her current role as chair of OCP’s industry liaison team. Her perspective illuminates both the technical challenges operators face and the collaborative frameworks emerging to address them.

Rapid Adoption Reshapes the Market

As data centers continue to evolve in the race to support AI-enabled workloads, Kelley noted that scalability has emerged as the primary challenge facing operators today. During our conversation, she cited an OCP keynote by Meta’s head of infrastructure, Dan Rabinovitsj. What once took multiple years now happens annually, leading him to compare current deployment cadences to consumer electronics rather than traditional data center timelines. “That was a big insight for me,” she said. “I live and breathe in this space, but it is a real insight to make that comparison.”

With this primary challenge of scalability come a host of secondary challenges, including cooling this infrastructure, and preparing for liquid cooling. Before 2022, more than 90% of data centers relied on traditional air cooling. In just two years, liquid cooling adoption has surged to approximately 30% of the market. This rapid acceleration has driven significant growth in coolant distribution units (CDUs), the critical infrastructure components that deliver coolant from chips to distribution systems. Recognizing this need, at the 2025 global summit, OCP announced a new working group focused on CDU specifications and best practices.

Driving Standards Development with Industry Partnerships

As the industry faces these challenges, collaboration and defining industry standards become more important than ever. At OCP, Kelley is chair of an industry liaison team that connects external standards organizations to OCP. This year, the team had two announcements to make. First, OCP and the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) signed a memorandum of understanding, establishing formal collaboration to eliminate duplicative efforts and create clearer pathways for standards development. In addition, a new liaison role connects OCP directly into ASHRAE’s processes, ensuring thermal management standards align with open infrastructure development. Second, ASTM International launched a new subcommittee on insulating fluids for immersion cooling applications, with a member of the industry liaison team working with that organization as well.

In another example of the importance of collaboration, Kelley highlighted the Universal Quick Disconnect (UQD) specification 2.0, which addresses quick disconnects within the cold plate cooling loop from the chip to the CDU.

“We had many different technologies…There was also a lot of proprietary designs, and this was creating a lot of problems and challenges within the industry,” she said. “Specification 2.0 was one of the first within liquid cooling to come out and really make standards more readily available to address the challenge of interoperability and heterogeneity within the data center.”

Embracing Hybrid Approaches to Liquid Cooling

Looking ahead, Kelley sees a future that will likely involve hybrid cooling strategies rather than single-solution deployments. While direct-to-chip cooling currently dominates new deployments, Kelley anticipates growing adoption of immersion cooling as workloads demand complete thermal management for entire compute stacks. Cooling networking equipment, memory modules, and storage alongside compute will become increasingly important. Immersion cooling’s ability to capture 100% of generated heat makes it a valuable complement to direct-to-chip solutions, particularly interest in heat recapture and reuse rises.

The TechArena Take

As AI-enhanced workloads continue driving unprecedented infrastructure demands, the industry’s ability to standardize rapidly will determine whether operators can deploy at scale while maintaining flexibility for future innovation. Kelley Mullick’s leadership in standards development through OCP demonstrates how open collaboration can accelerate adoption while maintaining interoperability. Organizations that engage with standards bodies today and build relationships across the ecosystem will be best positioned to capitalize on the liquid cooling transition reshaping data center design.

For more information about Avayla, visit avayla.net. To learn about OCP’s Cooling Environments working group and standards development, visit the OCP wiki.

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From #OCPSummit25, this Data Insights episode unpacks how RackRenew remanufactures OCP-compliant racks, servers, networking, power, and storage—turning hyperscaler discards into ready-to-deploy capacity.

As enterprises move artificial intelligence (AI)-based solutions further into production, inference speed is becoming a key factor in whether deployments succeed or fail. Real business value, and real infrastructure challenges, lie in how quickly models can generate responses for end users.

In a recent TechArena Data Insights episode, I spoke with Val Bercovici, chief AI officer at WEKA, and Scott Shadley, director of thought leadership at Solidigm, to explore how inference workloads are exposing infrastructure bottlenecks that threaten AI economics. Their conversation revealed why a metric called time to first token has become essential for measuring inference performance, and how storage architecture designed for this phase of AI can transform both productivity and profitability.

The New Currency of AI Performance

In a relatively short time, one metric has emerged to measure AI responsiveness: time to first token. As Val and Scott explained, this is a measure of the time it takes for a model to respond to a given prompt. It has emerged as a key metric because it directly translates to business value. “Time to first token literally translates to revenue, OPEX, and gross margin for the inference providers,” Val said.

As a concrete example, Val cited real-time voice translation, where instantaneous responses are critical to natural conversation. “Who wants to wait an awkward, pregnant pause of 30, 40 seconds for a translation?” Val asked. “We want that to be real-time and instantaneous, and time to first token is a key metric for that kind of use case.”

The metric matters because it reflects deeper infrastructure realities for AI inference workloads. Behind the response to every prompt lies a complex process that can be considered in two phases. In the pre-fill phase, prompts are converted into tokens and then expanded into key value (KV) cache, essentially the working memory of the large language model. Then in the decode phase, the model generates the actual output users see. Graphics processing units (GPUs) are currently being asked to do both at once, which is, in Val’s words, “a very expensive kind of context switching,” and the latter phase makes extreme demands of memory as well.

Rethinking Storage Architecture for AI Workloads

The conversation revealed how AI workloads differ fundamentally from traditional computing. Modern GPUs contain over 17,000 cores compared to a CPU’s hundred cores, creating entirely different performance requirements. This architectural shift demands a fresh approach to storage design, one that treats solid-state drives (SSDs) not merely as storage devices, but as memory extensions.

WEKA’s NeuralMesh Axon technology demonstrates this evolution. The solution embeds storage intelligence close to the GPU and creates software-defined memory from NVMe devices, allowing inference servers to see NVMe storage as memory, delivering memory-level performance from SSD hardware. This approach addresses one of inference computing’s most significant challenges: providing sufficient memory bandwidth to feed GPU cores without incurring prohibitive costs.

The Assembly Line Problem

One of the discussion’s most striking revelations centered on what Val termed the “assembly line” problem. While data centers optimized for AI are often described as “AI factories,” AI inference today operates more like a job shop than an assembly line. Data movement remains inefficient, causing expensive re-pre-filling operations and consuming kilowatts each time.

This inefficiency manifests in real-world constraints that AI users encounter daily. The rate limits imposed by AI service providers reflect the genuine economics of token generation. Coding agents and research tools that consume 100 to 10,000 times more tokens than simple chat sessions can’t be served profitably at current infrastructure costs, forcing providers to limit access even to customers willing to pay premium prices.

The Path Forward for Enterprises

Scott and Val offered practical guidance for IT leaders navigating the transition from proof-of-concept projects to production AI deployments. Scott stressed the importance of aligning hardware and software planning, noting that AI infrastructure demands closer collaboration between traditionally siloed teams. Val encouraged leaders to approach AI infrastructure with fresh perspectives, setting aside assumptions from previous technology generations.

The TechArena Take

As AI moves from experimental projects to production workloads generating measurable business value, infrastructure choices increasingly determine competitive advantage. Organizations that optimize storage architecture for token economics position themselves to scale AI profitably, while those applying traditional storage approaches risk creating bottlenecks that limit innovation. The enterprises that act decisively today in implementing high-performance storage architectures designed specifically for AI workloads will find themselves better positioned to capitalize on AI’s transformative potential.

For more information on WEKA’s AI infrastructure solutions, visit WEKA.io. Learn about Solidigm’s AI-optimized storage innovations at solidigm.com/ai.

For 15 years, Scality has operated at a scale most enterprises never contemplate. Its customers routinely manage petabytes, tens of petabytes, and now exabytes of unstructured data. My recent conversation with Paul Speciale, chief marketing officer of Scality, alongside Jeniece Wnorowski from Solidigm, revealed how this software-defined storage pioneer is navigating a shift in how organizations think about data protection in an era of ransomware threats and AI workloads.

Shifts in the Data Protection Landscape

The ransomware threat has fundamentally changed the data protection conversation. As Paul noted, you can’t go a day without seeing news of a cyber attack, and this constant barrage has added a new priority for chief information officers: in addition to backup speed, recovery speed is now top of mind. Organizations need their backup systems to serve as insurance policies that can quickly resurrect operations after an incident. This demand requires special architectural considerations and performance characteristics from the backup system.

AI also has introduced additional complexity to the data protection equation. First, the integrity of data feeding AI systems becomes paramount. “Imagine training your AI on data that’s been tampered with or has some kind of integrity constraints,” Paul said, raising the possibility of the potentially catastrophic outcomes. Second, AI itself becomes a tool for both attack and defense. While Scality explores embedding AI capabilities to detect suspicious access patterns and questionable payloads, ransomware actors are simultaneously weaponizing AI for sophisticated phishing attacks and impersonation schemes.

The Flash Storage Tipping Point

The flash storage revolution is reshaping Scality’s deployment patterns in ways that would have seemed unlikely just years ago. Historically, Scality’s massive capacity deployments relied primarily on high-density hard disk drives (HDDs), with flash comprising only 1-2% of total capacity to accelerate metadata operations and data lookups. The converging costs between flash and HDD storage, combined with performance demands from analytics and AI workloads, are driving increased adoption of all-flash configurations.

Paul cited a conversation with a major analyst firm that reinforced this trend: AI models in cloud environments are already training on object storage backed by flash. The same pattern is emerging for on-premises deployments. Even fast backup operations can benefit from all flash storage. “Why? Because again, it’s back to this restore equation,” he explained. “If I have flash, I can do a faster job of restoring.”

Software-Defined Advantage in a Competitive Market

Paul emphasized Scality’s software-only approach as a core differentiator in an increasingly competitive storage market. By remaining hardware agnostic, Scality can leverage best-of-breed components like Solidigm’s high-capacity SSDs while optimizing for specific media characteristics. This flexibility extends to energy efficiency, performance tuning, and media-specific optimizations.

The conversation revealed Scality’s vision toward becoming a data platform, extending their reach beyond storage. “And what is a platform?” he asked. “A platform is something that has a series of services. It might be a search service, a data cleansing service, a vectorial database for AI.” Paul sees software vendors as holding advantages in delivering these integrated service offerings given they are not tied to specific servers or form factors.

Object Storage and the Path Forward

Object storage’s mainstream emergence represents another important industry change. After 15 years of evangelizing object storage’s benefits, Paul sees the market finally recognizing its fundamental advantages for managing unstructured data at scale. Object storage’s inherent scalability, combined with its understanding of metadata and data characteristics, positions it ideally for immutable data protection and AI workloads. The hyperscalers have already validated this approach through Amazon S3 and Azure Blob adoption for AI applications. “The opportunity is huge as an object storage player in that arena,” he said.

The TechArena Take

Scality’s 15-year journey demonstrates the importance of being able to adapt alongside shifting enterprise priorities. Storage solutions must balance performance, resilience, and flexibility to meet the growing demand for data restoration capabilities and to address the changes rising from AI’s dual role as both a workload and security tool. As object storage enters the mainstream and flash economics continue improving, organizations managing massive unstructured data volumes will increasingly depend on software-defined approaches that optimize across media types and deployment models. Scality’s platform vision, rooted in pure software architecture and object storage expertise, positions the company to address these converging demands.

For more information on Scality’s data protection solutions, visit scality.com.

Allyson Klein and co-host Jeniece Wnorowski sit down with Arm’s Eddie Ramirez to unpack Arm Total Design’s growth, the FCSA chiplet spec contribution to OCP, a new board seat, and how storage fits AI’s surge.

The artificial intelligence “surge” is here, and with it, data center infrastructure is fundamentally changing. From increased demand for liquid cooling to a rise in interest for co-location services, both bleeding-edge and well-established solutions are being called into play to answer AI’s appetite for power, cooling, and data access. I recently explored this phenomenon with Glenn Dekhayser, global principal technologist at Equinix, and Scott Shadley, leadership marketing director at Solidigm, to understand how enterprises are navigating AI’s infrastructure demands.

The fundamental challenge facing enterprises deploying AI can be summarized in two words, according to Glenn: “power and heat.” Where graphics processing units (GPUs) once served as passengers in computing architectures, they now drive the entire infrastructure bus, and the power they demand is staggering. An estimated 97gigawatts of new power will be required for all registered data center projects by 2030, and when one average nuclear reactor puts out 1.4 gigawatt, “The math doesn’t work,” as Glenn said.

These power requirements create cascading effects throughout the infrastructure stack. Glenn noted that not every organization needs the highest-performance solutions, and liquid cooling becomes necessary only when rack density exceeds 40 kilowatts (kW). However, when direct-to-chip liquid cooling can capture and dissipate up to 1,000 times the amount of heat that air can, enterprises deploying next-generation GPUs must confront the requirements to implement this technology. This transition fundamentally changes total cost of ownership calculations and operational complexity for new infrastructure investments.

The full effects of AI’s increasing prevalence remain to be felt, but as Glenn noted, AI is driving change across all industries. While generative AI has advanced adoption so that “everybody has a need for” AI now, different forms of machine learning are also being used to solve domain-specific problems. “Every conversation has some angle to it,” he noted. “Whether you’re to be a consumer or provider or some middle service provider for data.”

Beyond dealing with power and heat, enterprises are adopting a mix of infrastructure strategies to adapt to the needs of AI workloads. For example, contrary to predictions that AI would centralize workloads in public clouds, enterprises are increasingly turning to co-location for AI deployments. The driving factors extend beyond simple cost considerations. The rapid pace of AI innovation means that the public cloud provider chosen two years ago may not offer the optimal AI services, capacity, or specialized models an organization needs today. Co-location provides the connectivity and flexibility to leverage various services without vendor lock-in, while maintaining control over data sovereignty and performance.

Perhaps nowhere is AI’s impact more visible than in storage strategy. Scott outlined how different AI workloads create distinct storage demands, and how innovative techniques can make the most of storage in all portions of the AI pipeline. For example, Solidigm has shown that retrieval-augmented generation (RAG) tasks can be offloaded from expensive distributed random-access memory (DRAM) to optimized solid-state drives (SSDs), and that performance can actually be improved while lowering costs by doing so.

Both experts emphasized that successful AI infrastructure requires holistic thinking across compute, storage, and interconnect. Rather than isolated decisions, enterprises need integrated solutions that can adapt to rapidly changing requirements.

The TechArena Take

The AI infrastructure revolution is forcing enterprises to balance competing demands: the need for high-performance computing against power and cooling constraints, the desire for cutting-edge capabilities against cost considerations, and the requirement for agility against infrastructure investments. Equinix and Solidigm demonstrate how thoughtful collaboration can address these challenges through flexible, efficient solutions that scale from current needs to future requirements.

As AI continues its rapid evolution, organizations that invest in adaptable, well-connected infrastructure today, while maintaining control over their data platforms, will be best positioned to capitalize on tomorrow’s AI innovations. The key is not predicting exactly what AI will become, but building the foundation to respond quickly when it does.

For more insights on Equinix’s AI infrastructure solutions, visit blogs.equinix.com or connect with Glenn Dekhayser on LinkedIn. Learn more about Solidigm's AI-focused storage innovations at solidigm.com or reach out to Scott Shadley on social media platforms.

For decades, redundant array of independent disk (RAID) technology has quietly protected enterprise data, operating as a reliable safeguard designed in an era when hard disk drives were the only storage option available. That legacy architecture is now colliding with the demands of AI workloads, creating a performance gap that enterprises can no longer afford to ignore. As organizations invest heavily in infrastructure to optimize graphics processing units (GPUs), they’re discovering that traditional data protection methods can become a significant bottleneck preventing them from maximizing their AI investments.

During a recent TechArena Data Insights episode, I explored this challenge with Davide Villa, chief revenue officer at Xinnor, and Sarika Mehta, senior storage solutions architect at Solidigm. Our conversation revealed how the transition from hard drives to high-capacity quad-level cell solid-state drives (QLC SSDs) is forcing a fundamental rethinking of data protection strategies for AI environments.

The GPU Utilization Challenge

Davide framed the issue clearly: AI infrastructure is designed around GPUs, with storage often treated as an afterthought. That approach creates significant risks. A leading truck manufacturer shared that every AI-based simulation job they run costs more than $2 million. If they lose data and must rerun a job, they’re facing substantial financial consequences.

The challenge extends beyond data loss prevention. GPU idle time carries steep costs, making it essential to maintain full system performance even during drive failures. Traditional RAID solutions that were designed for hard disk drives (HDDs), a slow media, struggle to meet this requirement. The performance characteristics of modern NVMe drives—capable of delivering tens of gigabytes per second in read and write, and multi-million input-output per second (IOPS) in random operation—require data protection solutions designed specifically for that level of parallelism.

The Performance Delta

Testing conducted by Solidigm and Xinnor revealed striking performance differences between traditional and modern data protection approaches. Rebuilding a 61.44 terabyte (TB) drive took just over five hours with Xinnor’s xiRAID solution compared to more than 53 hours with traditional Linux OS RAID (MD/RAID).

More importantly, those measurements represent system performance during idle rebuild operations. When the same tests ran with heavy workloads active, the performance gap widened dramatically. Xinnor’s solution completed rebuilds 25 times faster while maintaining full system performance. “So you can still run your AI workload…as if nothing happened. And in the background, our software is rebuilding the drive, recovering the data, and avoiding any data loss,” Davide emphasized.

The Storage Transition

Sarika highlighted how the shift from hard drives to QLC SSDs is enabling these improvements. Solidigm’s QLC drives deliver 11 times more write bandwidth and roughly 25 times more read bandwidth compared to 30TB hard drives. Those performance characteristics, combined with capacity advantages—122TB SSDs versus 32TB maximum for hard drives—create compelling economics for AI deployments.

The transition also addresses power and space constraints that have become critical considerations. As GPU power consumption claims the majority of data center power budgets, storage must maximize capacity per watt. High-capacity QLC drives deliver the necessary performance for AI workloads while optimizing the infrastructure footprint.

Another factor driving adoption is the warming of storage tiers. “Data has resided on cooler tiers before, which were primarily served by hard drives. But with AI taking off, those tiers are warming up. The performance that hard drives were providing before is no longer sufficient,” Sarika explained. That shift makes the raw performance of QLC SSDs essential for preventing GPU idling.

Software as the Enabler

Davide emphasized that hardware performance alone isn’t sufficient. Software plays a crucial role in enabling reliable performance at scale. AI deployments require clusters of drives, not individual units. The challenge lies in aggregating multiple drives into larger pools while maintaining the performance characteristics of individual devices. “This can only be done through software implementation,” Davide said. “So that’s exactly what we do. We try to maximize the aggregated performance of what Solidigm brings to the market.”

Xinnor’s approach focuses on maximizing what hardware can theoretically deliver as the industry transitions from PCIe Gen 4 to Gen 5. That optimization ensures organizations can fully leverage the capabilities Solidigm brings to market while maintaining the data protection AI workloads require.

The TechArena Take

The convergence of high-capacity QLC SSDs and modern data protection software represents a meaningful advance for AI infrastructure. Organizations that recognize storage and data protection as strategic components will be better positioned to maximize their GPU investments and avoid the costly consequences of system degradation or data loss. As AI workloads continue to evolve and drive capacity demands higher, the gap between legacy RAID approaches and modern solutions will only widen.

For more information about Xinnor’s data protection solutions, visit xinnor.io. Learn more about Solidigm’s AI-focused storage innovations at solidigm.com.

OCP Summit put a spotlight on something I’ve been watching for a while: AI has turned the data center into a living system. The optimization target isn’t a single server box anymore. It’s the confluence of power, liquid cooling, interconnects, storage tiers, and software orchestration, all moving in step to serve wildly dynamic workloads. I was lucky enough to moderate an AI Factory panel last week with CoreWeave’s Jacob Yundt, Dell’s Peter Corbett, NVIDIA’s CJ Newburn, Solidigm’s Alan Bumgarner, and VAST Data’s Glenn Lockwood. These seasoned data center veterans have experience from across the value chain and provided some fantastic insights on how swiftly data center innovation is moving to address customer demand.

Designing for Racks and Multi-Racks

The clearest through-line in the conversation was the industry shift from box-level thinking to rack- and row-scale system design. That change cascades into everything from how you bring in power and manage heat to how you route traffic, where you place data, and how you orchestrate workflows. Jacob framed the pivot crisply, discussing CoreWeave’s move from “thinking of compute as individual units” to racks, rows, and entire data centers, where “everything just needs to work together… power delivery, liquid cooling, networking, storage.”  

Glenn jumped on this foundation, explaining why VAST doesn’t merely see that as just a need for more network connectivity but a call for application-specific disaggregation. He clarified that rack-scale and scale-up interconnects lets you design for how the work runs, not just push everything through a generic L3 and hope for the best. He also pointed to checkpointing patterns and the pairing of global shared storage with node-local SSDs to maximize performance and economics.  

“Feed the GPUs”: Idle Time is Lost Opportunity and Unbelievable Cost

In AI data centers, compute utilization, more than ever, defines the business outcome. Peter outlined Dell’s view on the topic, plainly explaining that ensuring high utilization means feeding accelerators while not recomputing what you already computed. Operators need to build the surrounding storage and network capabilities so intermediate results and checkpoints can be reused at speed.  

That’s harder than it sounds because the software surface is changing at breakneck pace. CJ put a timestamp on it, reflecting NVIDIA’s torrid pace of innovation: “The only thing I’m really confident in is that it’ll be completely different in three weeks.” He emphasized that orchestration must continually decide where data should live, when to move it, and when remote access beats relocation.

Jacob echoed this sentiment stating, “The pace of deployment right now is unlike anything I’ve ever seen… the cost of getting it wrong… is so expensive… if your network or storage is not fast enough.”  When considering the scale that CoreWeave is operating, it was clear that guessing wrong here could make or break financial success.

Data Management is Ready for its Close-Up

While it was no surprise that storage was a central discussion point of the panel… after all, Solidigm did host this breakfast… the fervor with which the panelists spoke about storage underscored its architectural importance to this era of the data center. Their message? Stop relegating data plumbing to a downstream team. From metadata and indexing to checkpoint lifecycle and synthetic-data governance, data is foundational to the AI pipeline.

Peter walked through why. Training, inference, RAG, and fine-tuning each have distinct data modes and retention demands. Checkpoints must be instantly restorable and archived for reproducibility, while the generated/ simulated data used to train other LLMs needs cataloging at massive scale. He went further forecasting that “we could see a hundred-x increase” in those volumes.  

On the abstraction side, CJ argued for higher-level interfaces that let users express intent, “just go get my data wherever it is,” while the stack decides placement, sharding, staging, and presentation under the hood. That decoupling gives vendors freedom to innovate behind stable APIs as hardware and media evolve.

And the inside-the-enterprise reality check came from none other than Alan, sharing that  after a company-wide AI enablement push, Solidigm had discovered very quickly how unclean their data was. His sharing was met with many nods around the room and reflected my own discussions with enterprises that have shared the same scar tissue. To get the most out of AI, governance and operational control are no longer optional programs – they’re table stakes.

Efficiency is Required Everywhere

With the financial and resource-based investment in AI factories, the focus on efficiency has never been sharper. Liquid cooling has moved from science project to assumed ingredient. Jacob didn’t mince words, stating the importance of this innovation for companies like CoreWeave. He explained that the next frontier is tying everything together so when a 20-line command is about to launch and burn tens of megawatts, the power and cooling systems respond proactively, not reactively.

Peter built on this, highlighting why even storage needs thermal re-thinking. Multi-kilowatt drive enclosures are real, and standards groups including SNIA and OCP are working on how to cool serviceable drives in high-density racks, because you still need to pull and replace them safely.

Glenn then took the conversation further, uncovering a somewhat silent efficiency killer. He explained that when something fails or stalls, entire clusters idle and argued for communities like OCP to push shared semantics for reliability in practice so we can identify link flaps, hanging collectives, and other at-scale “ugly bits” quickly, rather than burning cycles waiting for the system to realize that it’s reached an unhealthy state.  

One culprit in this equation is how storage granularity can wreck energy math for certain vector-style workloads. CJ provided an example: reading 16–32KB NAND pages to ID ~512 bytes of useful data leads to absurd power budgets. The fix? Targeting coordinated innovation across drives, controllers, firmware, and software to reduce over-fetch and align I/O to what a workload actually needs.  

Alan added a note on fabric efficiency at scale, explaining that he’s seeing reports of very high utilization on enormous clusters, which are impressive, but it makes the stack hypersensitive to tail latencies and congestion. Getting protocol/ software choices right is what keeps utilization sustained, not just peaky.  

Standards are Required…and Need to Innovate Themselves

As veterans of the data center industry, the panelists knew well that open standards are an innovation multiplier. Peter put it succinctly, stating that standards have been essential for storage innovation across hardware and protocol interfaces because they let multiple vendors innovate behind them and lower barriers to entry for newcomers.

But when you are moving so quickly, can traditional storage approaches keep up? CJ offered a contrarian view, stating that we shouldn’t rush to standardize designs that limit concurrency or add unnecessary complexity. Instead, we should run experiments, share data, then bring the minimum viable interfaces to standards bodies so we minimize time to useful production without locking in untested ideas.

A 2030 Vision: One Mega-Rack Fueled by Lots of Goop

If you compress the future that our panel described into a single image, it’s this: An open chiplet designed fueled single rack, consuming 50 megawatts, surrounded by goop, running elegant software that is utilizing it. That was my summary of the panelists prognostications of a 5-year innovation horizon.

Jacob kicked us off with a view of  “football fields of infrastructure for power and cooling,” feeding one mega-rack at ~50 MW. This was not simply a joke, but the logical endpoint of density, liquid loops everywhere, and efficiency work that compresses visible compute while everything around it scales out of sight.

Glenn took the idea further, claiming that the “one rack” only works when it’s surrounded by the unglamorous machinery that turns data into answers. He explained that the AI factory of the future would be full of “goop,” the power, cooling, storage and networking required for compute, and at the center GPUs actually driving insights.  

On software, CJ pushed the same abstraction thread forward. He sees a future where operators declare intent (“go get my data wherever it is”), while the stack optimizes where data lives, how it’s sharded, and how it’s staged/presented across heterogeneous building blocks. That keeps innovation vibrant underneath stable interfaces as media, fabrics, and accelerators evolve.

On hardware, Alan expects innovation to move inside the package: by 2030, we’ll need new standards because we can combine different things inside an SoC in ways that aren’t possible at rack or PCB scales today. That’s the open chip economy showing up in real design choices including interfaces, subsystems, and thermal/ power tech that collectively ripples up into rack scale architecture.

And Peter widened the lens: the power envelope itself becomes a macro-catalyst. If the capacity required for AI is built primarily with low-carbon generation, the grid’s evolution can accelerate through economies of scale—changing siting, cooling, and heat-reclaim strategies along the way.  

The TechArena Take

AI isn’t just turning the data center into one big server, it’s turning it into a cohesive and interdependent organism. The amount of collaboration required to execute at this design point is unlike anything we have seen before. Aligning on open, agile interfaces that increase industry velocity will be critical to our collective success. Designing everything to remove inefficiency to the whole is now non-negotiable, and a seat is open for anyone at the table who has bold ideas for efficiency breakthroughs.

Midas Immersion Cooling CEO Scott Sickmiller joins a Data Insights episode at OCP 2025 to demystify single-phase immersion, natural vs. forced convection, and what it takes to do liquid cooling at AI scale.

No data center component is an island. While the industry conversation around AI infrastructure has focused heavily on graphics processing units (GPUs), a more fundamental truth is emerging: peak performance requires optimizing every component in the stack to work together. In fact, attempting to optimize any single element without considering the broader system inevitably limits what’s possible.

I recently had the opportunity to explore this interconnected reality with Solidigm’s Ace Stryker, product marketing director of AI infrastructure, and Jeniece Wnorowski, director of industry expert programs, to understand how storage requirements are evolving. During our conversation, it became clear that the most significant breakthroughs in AI infrastructure are coming from rethinking how data flows through the entire AI pipeline.

AI’s Expanding Diversity Shapes Storage Requirements

Ace started our conversation by emphasizing that we are still only a few years into AI becoming a prominent cultural force, and that its diverse potential is still yet to be fully uncovered. As AI-enabled workloads diversify with new models, tools, and solution stacks, the requirements for hardware—including storage—are diversifying as well. Truly understanding these requirements, however, means looking beyond storage specifications alone. “From a storage perspective, we’re really concerned about how the storage in an AI cluster interacts with the memory to deliver optimized outcomes,” Ace said. “Storage does not do the job on its own.”

The challenge extends beyond simple read-write speeds. Modern AI systems require careful orchestration between storage layers, host dynamic random-access memory (DRAM), and high-bandwidth memory on GPUs. Understanding how data moves between these memory tiers has become essential for IT architects planning next-generation infrastructure. As Ace noted, attempting to optimize storage in isolation limits the ability to understand what’s actually happening in the AI pipeline.

Challenging Conventional Wisdom

This focus on the interaction between memory and storage has led to research with surprising outcomes. Solidigm recently worked with Metrum AI to examine what happens when significant amounts of AI data are strategically moved from memory onto solid state drives (SSDs) in ways that weren’t typically considered.  

The companies used video from a busy traffic intersection and fed it into an analysis pipeline that generated embeddings and created a RAG database, then created a report about what happened in the video with suggestions for safety improvements. By offloading RAG data and inactive model weights from memory to SSDs, they achieved a 57% reduction in DRAM usage for a 100 million vector dataset. More surprisingly, queries per second actually increased by 50% compared to keeping data in memory, thanks to more efficient indexing algorithms in the SSD offload approach.

The implications extend beyond cost savings. The research demonstrated running the Llama 3.3 70 billion parameter model on an NVIDIA L40S GPU, a combination that normally exceeds the GPU’s memory constraints. For organizations looking to repurpose legacy hardware or deploy AI capabilities in edge environments with power limitations, this represents new possibilities for using hardware previously considered inadequate for modern AI-enhanced workloads.

The Density Revolution

While performance optimization captures headlines, capacity evolution tells an equally compelling story. Solidigm’s 122 terabyte (TB) drives, roughly the size of a deck of cards, represent just one milestone in a rapid progression that’s seen capacities jump from 30TB to 60TB to 122TB in a single year. The company has announced plans for 256TB drives, and as Ace said, “It’s not too long before you’re going to see Solidigm and others aiming at a petabyte in a single device, which was unfathomable even five years ago.”

These density improvements deliver practical benefits across the infrastructure stack. Higher capacity per drive means fewer physical devices required, reducing rack space requirements, power consumption, and cooling costs while maintaining the throughput AI-enabled workloads demand.

The Partnership Imperative

Throughout the conversation, Ace returned repeatedly to collaboration as Solidigm’s core operating principle. The company’s logo, an interlocking “S” design, symbolizes partnerships fitting together to solve complex problems. It’s reflected in their approach across the ecosystem, from working with NVIDIA on thermal solutions to collaborating with software orchestration leaders and cloud service providers.

This partnership focus acknowledges a fundamental reality: storage optimization happens within a broader system context involving networking, software orchestration, and compute resources. Solutions that work in isolation rarely deliver optimal outcomes at scale.

The TechArena Take

As AI-enhanced workloads continue their exponential growth, the organizations that understand storage as a strategic enabler rather than a commodity component will gain sustainable advantages. Solidigm’s research demonstrates that intelligent storage strategies can unlock performance improvements while simultaneously reducing costs and expanding deployment possibilities. For IT architects planning next-generation AI infrastructure, the message is clear: look beyond the GPU specifications and examine how data moves through your entire system. The gains not only in efficiency, but overall capability, may surprise you.

Learn more about Solidigm’s AI-focused storage innovations at solidigm.com or connect with Ace Stryker on LinkedIn.

In this episode of Data Insights, host Allyson Klein and co-host Jeniece Wnorowski sit down with Dr. Rohith Vangalla of Optum to discuss the future of AI in healthcare.

From hyperscale direct-to-chip to micron-level realities: Darren Burgess (Castrol) explains dielectric fluids, additive packs, particle risks, and how OCP standards keep large deployments on track.

From OCP in San Jose, PEAK:AIO’s Roger Cummings explains how workload-aware file systems, richer memory tiers, and capturing intelligence at the edge reduce cost and complexity.

Once defined by monolithic architectures and predictable workloads, today’s enterprise data center strategies are shaped by the explosive rise of AI, the realities of hybrid multicloud, and the mounting pressure of regulatory and efficiency demands. I recently spoke with Glenn Dekhayser, global principal technologist at Equinix, and Scott Shadley, leadership marketing director at Solidigm, who shared their perspectives on how enterprises are adapting, and what it will take to succeed in the years ahead.

The conversation began with an important insight on data center infrastructure from Glenn, who noted that AI has “10x’d” hybrid multicloud architectures. As he explained, organizations are grappling with where to deploy AI workloads—cloud, GPU-as-a-service, on-premises, or edge. As those workloads move to production, they’re driving a fundamental shift toward dense power solutions and liquid cooling as enterprises seek to control costs and performance.  

But the real transformation is in how organizations think about data itself, with “data-centric” strategy, which while complex in execution, comes down to a simple idea. “Whatever you’re doing, creating value starts with your data,” said Glenn. For enterprises trying to extract new value streams out of data, that means now workloads come to that data, rather than the reverse. Enterprises are creating entire data marts to reflect the change that data no longer has one-to-one relationships with applications, and instead, multiple applications access shared datasets.  

This centralized data approach addresses the reality that while workloads are relatively easy to deploy and orchestrate, datasets carry constraints: they’re slow to move, require governance, and face compliance and sovereignty requirements. In response to these challenges, Glenn said he counsels customers to create an “authoritative core,” one copy of active datasets on equipment you control in locations you can access.  

This core, of course, must be balanced with the ability to project data where it needs to be for optimal governance, compliance, cost, and performance. That could be in public clouds, at the edge, or in the core data center. Many enterprises, however, “Are starting to realize they’re not necessarily set up to take advantage of all those places,” Glenn said. “And if their data architecture...[isn’t] ready to accommodate this mobility, they’re going to find themselves at a competitive disadvantage.”  

As organizations work to upgrade their data architecture to avoid such a disadvantage, data center infrastructure technology advancements are keeping pace. Scott explained how storage technology in particular is evolving to meet the needs of new data center strategies. Large capacity drives help enterprises keep data close and store more while using less power, while performance drives enable fast work with the data at hand, so storage doesn’t become a bottleneck. As he summarized, “A modern architecture of flash tiering, flash plus hard drives, is becoming even more and more valuable.”  

When asked about the one question enterprise data center managers should consider that they weren’t thinking about five years ago, both experts converged on a theme: architectural flexibility for unknown future requirements. Scott emphasized not just focusing on capital expenses, but considering operational expenses and building systems with five-year operational efficiency in mind. Glenn emphasized the unknown, saying he often asks organizations how they are “architected for change” in a world where the next transformational service provider could emerge from completely unexpected origins.  

The conversation culminated in an analogy: if the best time to plant a tree was 50 years ago, the second-best time is now. For enterprises sitting on large datasets in public cloud environments, the cost and complexity of data mobility only increase with time. For organizations that aren’t yet “architected for change,” the time to remedy that is now, not in one to two years when their data lake will be even larger, and therefore more difficult and more expensive to move.  

The TechArena Take  

The enterprise data center conversation has evolved from optimizing known workloads to architecting for unknowable futures. Equinix and Solidigm’s insights reveal that success increasingly depends on maintaining data sovereignty while preserving access to innovation. Organizations that establish authoritative data cores with agile connectivity to diverse service ecosystems today will be positioned to capitalize on tomorrow’s transformational opportunities, whatever form they may take.

From OCP Summit San Jose, Allyson Klein and co-host Jeniece Wnorowski interview Dr. Andrew Chien (UChicago & Argonne) on grid interconnects, rack-scale standards, and how openness speeds innovation.

From OCP Summit 2025, Kelley Mullick joins Allyson Klein and co-host Jeniece Wnorowski for a Data Insights episode on rack-scale design, hybrid cooling (incl. immersion heat recapture), and open standards.

As AI spreads into every corner of the technology ecosystem, the industry is under mounting pressure to measure real performance consistently and transparently. Whether it's hyperscale training runs, edge deployments, or domain-specific AI applications like automotive, the need for shared benchmarks is coming into sharper focus.

At the AI Infra Conference in Santa Clara, Jeniece Wnorowski and I sat down with David Kanter, Founder of MLCommons and Head of MLPerf, for a Data Insights conversation that captured a pivotal moment for the benchmarking community. 2025 has been, as Kanter described it, “the summer of MLPerf.”

Benchmarking Matures as AI Expands  

“This has actually been a really breakout year for us,” Kanter said. “...In the last couple months, I was jotting an e-mail down to the team and I was describing it as the summer of MLPerf.”  

The momentum is tangible. He went on to describe new initiatives that reflect how AI adoption is spreading into new domains.

“We talked about MLPerf Storage. We also have MLPerf Automotive, which came out very recently, [and] MLPerf Client. And so, part of this is, as we’re seeing AI being adopted in more and more places, we have to come in and help fill those gaps. Storage was sort of us working with some storage folks to spot some coming challenges and...automotive was sort of in our response to the automotive folks saying, ‘You know, OK, we are going to be using more AI, we’re making more intelligent vehicles, we need to get our hands around this.’”

These expansions reflect the growing role of benchmarking beyond traditional training and inference in hyperscale environments. Storage and automotive, in particular, highlight the diversification of AI workloads across industries.  

Still in “Benchmarking Grade School”  

Kanter offered a candid perspective on MLPerf’s age compared to other well-established benchmarks.

“Some of the most established benchmarking organizations that we look up to are 30 years old and they’ve been honing their craft,” he said. “We’re seven years old...We’re still in grade school. The babies of benchmarks.”

That self-awareness underscores the pace at which MLPerf is evolving. Unlike existing benchmarks that matured slowly over decades, MLPerf operates in a landscape where new accelerators, models, and deployment modalities emerge every quarter.

“Just keeping a pace of things is both exciting, a little stressful, and a bit of a challenge,” he said.  

Building Community Through Standards  

Kanter emphasized that MLCommons isn’t just about benchmarking—it’s a volunteer-driven community.  

“I always say to anyone...if any of these resonate with you, please show up. We are a community of volunteers...It came together with a bunch of folks who just saw a problem and said we all want to solve it together.”  

Beyond MLPerf, he stressed that MLCommons works on a ton of projects around data including AI risk and reliability, research, and foundational datasets and schemas.

“How do we standardize making data accessible to AI?” he said. “How do we make AI more reliable and responsive to what humans want?”  

That openness has been key to MLPerf’s rise as the de facto performance yardstick across the AI ecosystem. Submissions from vendors large and small now shape how the industry evaluates real-world performance.  

Why Benchmarks Matter Now  

As enterprises wrestle with where to deploy AI—in centralized facilities, at the edge, or in hybrid environments—benchmarks like MLPerf give them objective tools to evaluate trade-offs. They’re also increasingly relevant for sustainability strategies, allowing organizations to understand performance per watt or per dollar across platforms.  

In an era of rapid infrastructure buildout and diversification, shared benchmarks provide a common language for vendors, operators, and developers alike.  

TechArena Take  

Benchmarking is becoming a strategic force in AI infrastructure. What started as a niche performance measurement initiative has grown into a foundational layer that shapes how chips are designed, systems are built, and deployments are optimized.

David Kanter’s reflections highlight both the rapid maturity of MLPerf and the youthful dynamism of a community racing to keep up with AI’s evolution. As AI spreads into storage systems, automotive environments, and edge devices, the role of shared, open benchmarks will only deepen.  

The bottom line: In the AI era, you can’t scale what you can’t measure. And MLCommons is ensuring the industry has the tools—and the community—to do just that.

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As AI workloads push data centers to their physical and environmental limits, the industry is waking up to a hard truth: today’s cooling methods can’t sustain tomorrow’s compute demands. The rise of multi-megawatt racks, unprecedented water consumption, and surging energy use are forcing operators to rethink infrastructure from the ground up.

At the 2025 AI Infra Summit in Santa Clara, Jeniece Wnorowski and I sat down with Jonathan Ballon, CEO of Iceotope, for a Data Insights interview on why it’s imperative to consider sustainability in AI infrastructure buildout—and how Iceotope is redefining cooling technology both at the edge and in the datacenter.  

The Efficiency Imperative

AI’s rapid growth has triggered exponential increases in power and water consumption. Traditional air cooling is struggling to keep up with the thermal profiles of modern AI accelerators, while water-intensive cooling towers and evaporative systems are under scrutiny for their environmental impact.

“When you look at the amount of resources that are being consumed right now—whether it’s power or water—it’s unsustainable,” Ballon said.

This urgency was one of the key factors that drew him to lead Iceotope. The company’s datacenter liquid cooling technologies use 96% less water than conventional systems and reduce cooling power requirements by up to 80%. Their edge AI solutions can be deployed in almost any environment, without the use of existing facility water or dry chillers.

Those kinds of efficiency gains aren’t just good for the planet—they’re essential for the economics of deploying AI workloads. As data center campuses scale into the gigawatt range and high-throughput edge applications that require low latency continue to grow, sustainable cooling is becoming both a moral and a financial imperative.

Rethinking Infrastructure Without Forklifts

For enterprises, the efficiency conversation is tied closely to infrastructure planning. Many organizations are not starting from scratch—they’re retrofitting existing facilities or deploying AI workloads incrementally.

“I think enterprises need to think carefully about how they’re going to invest in new infrastructure,” Ballon explained. “What we’re seeing is enterprises that are looking for retrofit capability that doesn’t require forklift upgrades so that they can adopt liquid cooling gradually rather than having to do...forklift changes either in their existing infrastructure or in their new builds. Looking for technology that allows you to do that is super important. Otherwise, they could be making major infrastructure changes that could be outdated in three to five years, which could be a major risk.

That message resonates with enterprise IT leaders who must balance innovation with capital discipline. Liquid cooling solutions that integrate into existing footprints without major overhauls offer a pragmatic path forward.

Lessons from Hyperscalers

It’s tempting for enterprises to look at hyperscalers or “neo-clouds” as models for AI infrastructure strategy. But Ballon cautioned against simply trying to emulate the giants.

Instead, he suggests that enterprises use hyperscaler deployments as reference points while tailoring their strategies to their own operational realities. For many organizations, that means exploring modular, flexible solutions that enable growth over time rather than attempting to leap directly to hyperscale patterns.

New Edge Deployment Models

One of the most intriguing threads of the conversation centered on non-traditional deployment locations. Ballon and the hosts discussed the idea of deploying AI infrastructure in underused commercial spaces, like vacant office buildings, as part of an edge strategy.

While a hyperscaler wouldn’t take over an office suite to build something that isn’t at their usual scale, a retailer might, he said. As enterprises look for ways to bring AI closer to where data is generated, creative use of available real estate could accelerate edge deployments, particularly if efficient, compact, and quiet cooling solutions make it viable. Iceotope’s latest product line focuses on edge solutions that don't require access to facility water or dry chillers. Their fan-free, quiet operation makes them ideal for deployment in office environments.

A Legacy-Driven Mission

For Ballon, Iceotope’s mission goes beyond engineering.

“I’m at the stage of my career where I think about my kids, I think about the environment, and what legacy I want to leave behind,” he said.

By dramatically reducing water and energy use, Iceotope aims to make the environmental footprint of AI infrastructure compatible with a sustainable future. It’s a vision that blends technical innovation with a long-term view of responsibility—one increasingly shared across the data center industry.

The TechArena Take

Cooling is no longer an operational afterthought—it’s becoming a strategic lever for sustainable AI growth. Iceotope’s approach demonstrates how innovations in liquid cooling can enable enterprises to meet surging compute demands while dramatically cutting environmental impact.

The big insight from Ballon’s conversation: enterprises don’t need to mimic hyperscalers to succeed in the AI era. By focusing on retrofit-friendly technologies and purpose-built designs, they can chart their own path—one that balances performance, cost, and environmental stewardship.

As AI infrastructure spreads beyond massive campuses into distributed edge locations, energy efficient and water-saving cooling solutions will shape the future of data center design. Iceotope is positioning itself squarely at the intersection of sustainability and innovation.

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Modern storage infrastructure presents a complex balancing act. As solid-state drives (SSDs) evolve to provide performance levels demanded by artificial intelligence (AI) workloads, power consumption has grown alongside speed, prompting a necessary evolution in how organizations evaluate and optimize their storage investments.

During a recent TechArena Data Insights episode, I spoke about this phenomenon with Jeniece Wnorowski, director of industry expert programs at Solidigm, and Scott Shadley, director of leadership narratives at Solidigm. Our conversation revealed the complex factors affecting storage efficiency, and key areas organizations need to consider when undertaking efforts to optimize their systems.

Redefining Storage Efficiency with Modern Metrics

To set the stage for our conversation about storage efficiency, Scott noted that in his work with customers and partners, what’s critical is “Understanding how we manage budgets. And those budgets include power budgets and all the other aspects of building an efficient data center,” he said.  

Considering how finite resources are allocated has become increasingly important as modern flash-based storage products are being deployed in architectures that demand unprecedented performance levels. These demands have led SSDs to draw more power than ever expected, given they were designed to be both fast and power efficient.

The challenge, in fact, lies not in the technology, but in the metrics used to determine the best storage solution for use case requirements. As system demands increase, new measures are necessary to make architecture and procurement decisions. “We’ve always used the same metric, dollar per gigabyte,” Scott explained. “There’s a lot of new metrics that we’re focused on today, like watts per terabyte or terabytes per input/output operations per second…so we’ve evolved the ecosystem to talk through what a modern infrastructure looks like.” These measurements provide a more accurate picture of total system efficiency and help guide decisions from being about the fastest or the biggest drive to the right storage solution for the job.

Engineering Efficiency in Every Part of SSDs

While the legacy of SSDs is already rooted in efficiency, Solidigm is actively working on solutions to even further improve storage efficiency. For example, the company has worked with standards bodies and partners to optimize idle times. “These power states that we can put drives in make sure that they make the most of the power available to them. They have fast on, fast off, and things that you just can’t do with other aspects of storage infrastructure,” he explained

The architectural innovations extend beyond power states to fundamental design choices. For example, Scott detailed how Solidigm has long focused on optimizing the design of SSDs’ controllers, which can draw significant power if designed inefficiently. For ultra-high-capacity drives like their 122TB models, they’ve worked within the architecture and firmware design to keep only necessary components active as needed, which becomes critical when hundreds of drives populate enterprise racks.

Tackling System-Level Optimization for Maximum Benefit

Beyond the drives themselves, holistic system changes are critical to optimizing efficiency. Scott emphasized that modernization efforts must address both hardware and software components to realize systems’ full potential. Our discussion revealed a particularly intriguing challenge on the software side: legacy code optimization. Many applications originally designed for spinning media include built-in wait times, which become counterproductive with SSD deployment. These unnecessary delays waste power because systems continue drawing energy while waiting for data that has already arrived.  

Taking that challenge of comprehensive improvement a step further, Scott pointed out that drives are just one component of a larger system that must be considered. “It’s not about the drive,” he said. “It’s about the rack, and what you can do with the rack to make that rack more efficient.” A partnership Ocient, which builds a rack infrastructure that reduces the physical footprint required, shows the benefits of this approach. Reducing the footprint reduces the server count and rack-level power, which then translates into true reductions in total cost of ownership.

For organizations beginning efficiency overhauls, Scott recommended focusing on three key areas: software infrastructure optimization to eliminate unnecessary wait times, right-sizing storage performance to actual requirements rather than perceived needs, and leveraging portfolio diversity to match specific use cases with appropriate storage technologies. “Don’t just buy the fastest things, and even sometimes the biggest one isn’t what you need. We’ve got the portfolio to help you make yourself the most efficient system that can also scale,” he said.

The TechArena Take

The evolution of storage efficiency reflects a broader maturation in how enterprises approach infrastructure optimization. While IT teams wrestle with rising power consumption from high-performance storage, Solidigm’s focus on comprehensive efficiency demonstrates that the solution lies in addressing a complex web of factors. The companies prepared to not only work with efficient, modern drives, but to update their purchasing decision metrics and set aside piecemeal optimization strategies for a true systems-thinking approach will see the greatest benefits as workload demands continue to accelerate.

To learn more about Solidigm’s approach to efficiency and storage, connect with Scott Shadley on LinkedIn or explore Solidigm’s efficiency solutions at solidigm.com.

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Reliable. Economical. Even predictable. Those were the markers by which enterprises historically measured storage, like a necessary utility. The explosion of AI workloads, however, is forcing a complete reimagining of infrastructure architecture. The challenge is no longer having enough capacity. It’s orchestrating an intricate dance between compute, memory, and storage while managing a critical fourth element: power.

I recently had the opportunity to explore this transformation with Scott Shadley, director of leadership narrative at Solidigm, and Jeniece Wnorowski, director of industry expert programs at Solidigm, during a recent TechArena Data Insights episode. Their conversation revealed how storage companies are evolving from component suppliers to strategic infrastructure partners, and why the traditional approach to data center planning is becoming obsolete.

Finding the Right Balance as Everything Gets Bigger and Faster

Scott emphasized a critical reality that many organizations are desperately grappling with. Modern infrastructures, whether supporting AI workloads or enterprise applications, are requiring exponentially more capacity and performance to support data at unprecedented scales. But this data explosion is happening between two contrasting forces: the constraints of finite data center space, and unconstrained escalating power costs. “As everything gets faster and everything gets bigger, it starts to consume more power,” Scott said.

With these factors in tension, treating compute, memory, and storage as separate architectural components is no longer sustainable. “We spent a lot of time in the industry talking about compute, memory, and storage as three unique architectures,” Scott explained. “We’ve really gotten past that, and it’s now starting to look at how do we really do a better job of load balancing everything.”

Beyond Component Sales: The Strategic Partnership Evolution

With this shift, Solidigm is fundamentally redefining its role in the marketplace, becoming an infrastructure enabler and planning partner to its customers. The company offers deep expertise not just in storage technology, but in the workloads that storage enables. Scott noted that their team includes experts who have become more AI specialists than storage specialists, a strategic decision that enables them to anticipate infrastructure needs rather than simply react to them.

Scott explained that their approach has moved far beyond product specifications and benchmarks: “It’s not about pitching slides. It’s not about ‘buy this because it’s the next big thing.’ It’s really about having those conversations with folks to make sure they understand their need, we understand their needs, and they understand the solutions available.”

Engineering for Tomorrow’s Workloads Today

With this expanded role, Scott emphasized that successful infrastructure planning requires thinking beyond “what’s next” to the “next-next” scenarios: “The current products are amazing, and where we think they’re going to go is great. But if we’re not already thinking about next-to-next type of thing—and we’re not even talking 5-year, we’re talking 10-year roadmaps—you’re never going to be able to design the products fast enough.”

This forward-looking approach is particularly critical as emerging trends like edge computing, software-defined infrastructure, and composable architectures create divergent yet complementary demands. Edge deployments might require everything from ultra-compact drives for space-constrained environments to massive 122TB drives for high-density applications. Each scenario demands different architectural approaches while maintaining consistent reliability and integration characteristics.

The Economics of Future-Proofing

Long-term thinking also affects another facet of decision making: price. “I’m probably going to make a few people mad…but CapEx is not the issue,” Scott said. “I mean, everybody says, ‘I’ve got to spend as little as possible on CapEx,’ but CapEx is for today.”

Scott argues that infrastructure decisions made today will determine operational costs, and that the ability to maximize performance per watt, optimize rack density, and minimize cooling requirements can deliver operational savings that far exceed initial hardware cost differences. And these decisions don’t just affect costs, but capabilities. Today’s investments and architectural decisions determine whether organizations will be positioned to capitalize on emerging opportunities or constrained by infrastructure limitations.

The TechArena Take

Solidigm’s expanding role as an infrastructure partner reflects a broader industry transformation where success depends on understanding workloads as much as hardware specifications. The company’s emphasis on long-term roadmap alignment, customer collaboration, and holistic system optimization demonstrates how technology companies can create sustainable competitive advantages in rapidly evolving markets.

The most compelling aspect of their approach is the recognition that tomorrow’s data center challenges won’t be solved by simply making components faster or denser. Success will depend on architecting solutions that balance performance, power efficiency, and operational flexibility while anticipating workload requirements that haven’t yet fully emerged. Organizations that understand these dynamics, and that invest in understanding workloads rather than just selling products, will be best positioned to navigate the infrastructure complexities ahead.

Connect with Scott Shadley on LinkedIn or explore Solidigm’s AI-focused solutions at solidigm.com/AI to continue the conversation about future-proofing your data center infrastructure.

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