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This new voice of innovation piece from Vanguard Lead Data Engineer Vivek Venkatesan digs into FinOps and lays out a clear argument: scale alone is no longer a differentiator. The edge now lies in how intelligently teams design their stacks, manage cloud spend, and account for the energy and carbon behind every workload. Vivek walks through the shift from monolithic ETL to modular, open-table architectures like Iceberg and Delta Lake, where “big” isn’t the goal – adaptable is. He connects FinOps, sustainability metrics, and self-healing data platforms into a single design philosophy: treat cost, carbon, and performance as first-class signals, not afterthoughts. There’s a tough reality in the numbers: organizations still waste an estimated 30% of their cloud budgets on idle or misallocated resources. He encourages embedding efficiency directly into pipelines, measuring cost per insight and energy per transaction as carefully as latency and uptime. Read the full article: https://lnkd.in/gpPY5F55

This Thanksgiving, we’re feeling especially grateful for the people behind the platforms, silicon, and software that keep the AI era running. To our TechArena community of builders and innovators. To the IT and cloud architects who keep systems resilient when demand spikes. To the engineers turning power, cooling, and racks into AI factories. To the marketers who connect this unparalleled community of innovation. To our guests, sponsors, and clients who share their time and expertise in the arena. Thank you. You’re the reason we get to tell the stories behind the tech: how data centers evolve, how platforms scale, and how teams push the limits of what tech can do. However you’re spending the holiday, we hope you find time to unplug, recharge, and come back inspired for what’s next.

At Commvault's SHIFT event, CEO Sanjay Mirchandani laid out a compelling thesis. In an AI-first world, resilience isn’t a back-office insurance policy: it’s an operating model that needs to run in lockstep with your AI strategy. The company introduced ResOps (resilience operations) and Commvault Cloud Unity, a unified platform that ties together security, identity, and recovery. What does ResOps look like in practice? Commvault described it as a continuous loop in three stages: understanding and governing data and identities; detecting anomalies and threats in near-real time; and recovering cleanly and predictably at scale. While this “detect, respond, recover” may sound familiar to security teams, Commvault is pulling data protection and identity recovery into that motion as first-class citizens. Zooming out from the announcements, Allyson Klein called out a few trends to consider: 1. Resilience is becoming an operating model, not a product line 2. Identity and data are converging in resilience conversations. In an agentic world, identity mistakes and data mistakes are tightly coupled. 3. “Clean” is the new RPO. 4. AI is both the accelerant and the tool for response to vulnerabilities and attacks. If you’re rethinking infrastructure for agentic AI, this is a snapshot of how the resilience stack underneath is being reimagined just as aggressively as the AI stack on top. Read our full analysis: https://lnkd.in/giQkgQNs

In our latest blog post, Voice of Innovation Banani Mohapatra unpacks what she calls the new product DNA: an architecture where AI, data, and experimentation are the core building blocks of how products are imagined, designed, shipped, and improved. Banani walks through how the lifecycle is changing end to end: --Ideation becomes generative exploration, where LLMs and copilots help teams rapidly generate hypotheses, flows, and testable variants. --Design becomes data-infused creativity, with AI tools simulating user reactions and predicting engagement. --Build becomes experiment-ready engineering, where feature flags and metrics are wired in from day one. --Launch shifts from correlation to causality, with modern causal inference frameworks isolating true impact. --Learn is powered by automated knowledge loops, where agentic AI connects learnings across experiments and suggests the next move. You’ll also see a concrete example: a digital health platform for chronic care that uses this experimentation loop to personalize reminders, adapt interventions in real time, and steadily improve patient adherence. If you’re rethinking your product stack for an AI-native era, this piece is a field guide to what “inside the new product DNA” looks like in practice. Check it out: https://lnkd.in/gNQkrn6x

From the #SC25 show floor in St. Louis, our latest Data Insights episode dives into one of the most interesting stories in AI infrastructure right now: how Nebius is redefining the neocloud for enterprise AI. Host Allyson Klein and co-host Jeniece Wnorowski from Solidigm sit down with Nebius' Daniel Bounds to unpack what it really takes to build a purpose-built AI cloud that looks and behaves like a shared supercomputer. They talk about Nebius’ Token Factory PaaS, its deep roots in open source communities like Kubernetes and SLURM, and why full-stack engineering – from silicon choices and QLC-based storage to data center design – is the only way to keep up with AI demand. Daniel also breaks down how Nebius is serving foundation model builders and AI-native startups, why enterprise-grade security and compliance are now table stakes, and how partnerships with companies like Solidigm, WEKA, VAST Data, Meta, Microsoft and more are shaping what comes next. Watch the full episode to hear how Nebius is scaling from racks to global impact – and what their trajectory tells us about the future of neocloud infrastructure: https://lnkd.in/gRuz3ptu

#SC25 in St. Louis made one thing very clear: HPC has become the AI factory business. In our new recap, “Inside SC25: AI Factories From Racks to Qubits,” Rachel Horton walks through how the entire stack is being re-architected for AI – not just more FLOPS, but better factories. We look at data and memory platforms from WEKA, VAST Data, Microsoft Azure and MinIO, all focused on feeding GPUs with long-context data, exabyte-scale storage and agentic AI workflows. We dig into power and cooling with AIRSYS Global, Iceotope, Schneider Electric and Motivair by Schneider Electric, where concepts like Power Compute Effectiveness (PCE), liquid spray cooling and sealed edge clusters are turning thermal design into an economic lever for AI. On the server and fabric side, we cover Dell Technologies, Supermicro, ASUS, Compal, EnGenius Technologies and Intel Corporation, plus networking moves from Cornelis Networks and NVIDIA's BlueField-4 and Quantum-X photonics – all aimed at 800G-class, congestion-free AI fabrics. We also highlight exascale blueprints from Hewlett Packard Enterprise, AMD, Oak Ridge National Laboratory and Los Alamos National Laboratory, along with emerging quantum and photonics work from QuEra Computing Inc., Quantum Computing Inc., D-Wave, Phison Electronics USA and Hammerspace. If you’re designing the next generation of AI infrastructure, this is a single snapshot of who’s moving where – and how it all fits together as an “AI factory” stack. Read the full roll-up: https://lnkd.in/gZ6sBtQc

Stepping into a new cybersecurity leadership role rarely feels like a clean slate. You’re inheriting someone else’s tools, someone else’s incidents, and someone else’s definition of “secure.” Before you start rewriting roadmaps or sunsetting platforms, the smartest move is to listen—and ask better questions. In this new TechArena article, Voice of Innovation Tannu J. shares “15 Questions Every Cybersecurity Leader Should Ask on Day One.” It’s a practical guide for CISOs, heads of security, and security leaders who are walking into an ecosystem already in motion: existing risks, unspoken fears, and a culture that may or may not see security as a partner. The questions dig into how executives really think about risk, where data lives and who actually has the keys, which tools teams quietly avoid (and why), and how incidents really unfold when the pressure is on Read the full article: https://lnkd.in/ges9VTSK

GPU scarcity may get the headlines, but the real AI bottlenecks are hiding in your storage, networking, and orchestration stack. In this new Data Insights episode, Allyson Klein and co-host Jeniece Wnorowski from Solidigm sit down with Brennen Smith, head of engineering at Runpod, to unpack what it actually takes to run AI at scale on a GPU-dense cloud. Brennen explains how the economics of AI have flipped the traditional cloud model. When a single H200- or B200-class GPU can cost hundreds of thousands of dollars, every percentage point of utilization matters. That is pushing platforms like Runpod to go beyond raw GPU capacity and invest heavily in high-performance storage, low-latency networking, and smarter orchestration. Highlights from the conversation include: --Why “train once, inference forever” changes the long-term infrastructure game --How storage became the hidden bottleneck for AI workloads --Managing viral inference spikes without melting your data center or your power budget --The industry shift toward a “universal orchestrator” view of infrastructure --The next wave: convergence of infrastructure and software, where code self-declares the resources it needs Check out the full podcast here: https://lnkd.in/eXG28FbK #agenticai #cloudcomputing

Achieving peak FLOPS is the headline in many HPC conversations—but for a lot of real-world workloads, compute is actually waiting on something far more mundane: memory. In her latest article, TechArena Voice of Innovation Lynn Comp, Head of Datacenter Market Readiness at Intel Corporation, digs into why so many spectral simulations, graph analytics, and finite element codes are fundamentally memory-bound—and what that means for system design in the Xeon 6 era. Lynn shares a real example from a national lab: a spectral simulation that scaled beautifully to 128 nodes… then flatlined. The culprit wasn’t “not enough compute.” It was collapsing memory bandwidth per core starving the CPUs of data. Instead of just throwing more FLOPS at the problem, Lynn walks through how balanced performance across compute, memory, and interconnect changes the game. She explains how Xeon 6 brings P-cores and E-cores together on a unified I/O and memory interface, backed by high-throughput memory channels, large cache hierarchies, tuned NUMA, and low-latency coherence paths. The result: memory bottlenecks are less likely to fence off execution into narrow lanes, and mixed workloads—compute, I/O, checkpointing, and data orchestration—can coexist without gating overall performance. If you’re running memory-bound HPC applications, this is a must-read on why Xeon 6 should be on your shortlist for next-generation clusters: https://lnkd.in/dnXWfaFn

Fresh from #KubeCon + #CloudNativeCon North America in Atlanta, we talked with the Cloud Native Computing Foundation (CNCF), Devtron Inc., Komodor, and Dynatrace about how AI is fundamentally rewriting Kubernetes operations. In our latest deep-dive, Rachel Horton unpacks how three very different players are attacking the same problem: --Devtron is collapsing applications, infrastructure, and cost into a single control plane, then layering an “agentic SRE” interface on top. Think SRE co-pilot as a calculator, not a replacement, with FinOps and GPU visibility built in. --Komodor's Klaudia is a domain-specific, multi-agent AI SRE that can troubleshoot, self-heal fleets, live-migrate pods off spot instances, and still color inside the guardrails so teams don’t get burned by hallucinations. --Dynatrace is pushing AI observability from “here’s a pretty dashboard” to “here’s the root cause, the mitigation plan, and the PR we already opened—just click approve.” And they’re blunt about the new pressure: AI has to prove ROI, not just run hot and expensive. All of this is happening against CNCF’s new Kubernetes AI Conformance push, which aims to make AI workloads portable and interoperable across stacks. Our take: Kubernetes’ self-healing promise isn’t going away—it’s moving up a layer, into how organizations run, heal, optimize, and justify AI-era platforms at scale. Check out the full article:https://lnkd.in/gujf-uE6