💡 The invisible competitive edge of the AI era: Data centers We experience AI’s remarkable results as if they happen instantly — answering questions, generating images, understanding speech. But behind every interaction is an invisible infrastructure quietly powering massive computations: the data center. TEAM NAVER operates its own GAK data centers handling everything from design to operation in-house to strengthen its AI infrastructure capabilities. In particular, GAK Sejong, a hyperscale AI data center, is optimized for large-scale GPU clusters, with power, cooling, and networking built specifically for AI workloads. In this Tech Blog, we explore why data centers matter more than ever in the AI era — and how TEAM NAVER is building the foundation for the future of AI. 🔍 👇Check out the details on our tech blog! https://lnkd.in/gbfn4Xss
Data Centers Power AI Infrastructure
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The future of AI may depend on surprisingly old-fashioned factors: power, land, and infrastructure. As demand for compute skyrockets, data centers are becoming one of the most important—and complex—infrastructure assets in the global economy. BRG’s new issue of ThinkSet explores opportunities and tensions shaping this growth, from energy policy to legal disputes to infrastructure investment. Topics include: - Allocating the cost of rising electricity demand - Managing community opposition and environmental concerns - Valuing hyperscale data centers - Emerging risks in the AI data ecosystem Part one of this double issue is live, with additional articles arriving next month. Explore the issue: https://lnkd.in/de48NfJz #ThinkSet #AI #Infrastructure
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ABCDEFGHI… Is your AI strategy just a 31-second alphabet recital? Matthew Hardman from Hitachi Vantara APAC just proved why 99.9999% availability isn't enough for the GenAI era. While you're chasing GPUs, your “dark data” silos are causing a storage flap (literally). Wendy Koh and 王致炜 Joe Ong highlight the “One Data” fix for the 77% of firms failing at AI ROI with the VSP One reveal. It’s time to stop managing 100 tools and start scaling with native S3 and mainframe-to-AI consistency: https://lnkd.in/gMT8DumS #datastrategy #genAI #HitachiVantara #hybridcloud #techleadership #datainfrastructure #digitaltransformation
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Great read on how meta is linking geographically distributed AI clusters into a single, scalable, unified gigawatt-scale supercomputer with built-in resilience and fault mitigation. https://lnkd.in/gcPJGvYg
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The headline sums it up nicely. Strong coverage from iTWire’s David Williams on what we’ve been building at Everpure; and why it matters. AI infrastructure isn’t constrained by compute anymore. It’s constrained by data. That’s exactly what we’re solving with: • Data Stream which simplifies and automating the entire data pipeline, and available as an appliance. • Evergreen//One for AI — flexible, consumption-based infrastructure at scale. • FlashBlade//EXA — pushing the boundaries of high-performance storage Together, this is about removing bottlenecks and getting AI from experimentation to production—faster, simpler, and at scale. Appreciate the thoughtful write-up. Lee Nugent Anuya Upadhyay Daniela Vazille Altay Ayyuce Pratyush Khare Andrew Fisher GAICD Fredy Cheung Kellie Wheeler Sylvia Tong Wei Meng Ng Dan Corbeski Nick Paddon-Row Kishor Bhagwat Read more: https://lnkd.in/gfjyyAUm
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Great explainer on what we're up to at Everpure following Matthew Oostveen's chat with iTWire's David Williams yesterday. As Matt explains, this is all about building systems that shift AI from experimentation to production, quickly, simply and at scale.
The headline sums it up nicely. Strong coverage from iTWire’s David Williams on what we’ve been building at Everpure; and why it matters. AI infrastructure isn’t constrained by compute anymore. It’s constrained by data. That’s exactly what we’re solving with: • Data Stream which simplifies and automating the entire data pipeline, and available as an appliance. • Evergreen//One for AI — flexible, consumption-based infrastructure at scale. • FlashBlade//EXA — pushing the boundaries of high-performance storage Together, this is about removing bottlenecks and getting AI from experimentation to production—faster, simpler, and at scale. Appreciate the thoughtful write-up. Lee Nugent Anuya Upadhyay Daniela Vazille Altay Ayyuce Pratyush Khare Andrew Fisher GAICD Fredy Cheung Kellie Wheeler Sylvia Tong Wei Meng Ng Dan Corbeski Nick Paddon-Row Kishor Bhagwat Read more: https://lnkd.in/gfjyyAUm
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When scaling AI, rather than looking solely at the component level, organizations need to undertake system-level emulations that reflect the operating environment of an AI data center to optimize performance. https://lnkd.in/dk5Y5CKG
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AI Inference (actual production work) will become the hungry hippo (Moo Deng) of data center demand by 2030. Here is what you need to know from a McKinsey report on it: - AI data-center power demand will roughly triple by 2030, with Inference workloads becoming the majority, changing how and where compute capacity is built. - Hyperscalers will control ~70% of new AI capacity, making power access and build speed as critical as capital cost. - AI Inference forces data centers closer to users to meet latency needs, while AI Training drives large, remote AI campuses. - Power-constrained tier-1 markets are losing share, pushing hyperscalers to tier-2 regions with faster power access and much lower land costs. - Hyperscalers are reshaping the market by investing directly in power, modular builds, and retrofits to accelerate capacity delivery. The Bottom Line AI infrastructure growth is now constrained more by power and speed than by capital. Hyperscalers’ choices on location, power, and build models will dictate AI cost and availability. Falling behind these shifts directly risks slower AI deployment and higher operating costs. - https://lnkd.in/gyB2XAva #iCompaz #infosysCompaz #TransformationalChange #Infosys #Temasek #CIO #ciochat #technology #DigitalTransformation #innovation #cybersecurity #Transformation #data #AI #GenAI #AgenticAI
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Big Tech in big trouble - AI Data Centers push back by grass-roots politics (https://lnkd.in/e3ubknzj, https://lnkd.in/e8zxvp5u). The core of the problem for this "neo-luddite" movement is the strain on the power grid combined with increased electricity prices and environmental impact, including destruction of farmland (https://lnkd.in/eAxXfRzu). After all AI-generated main product (so far), deep-fake slop, is not edible 😉 https://lnkd.in/eJqKTXEs
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𝗔𝗜 𝗜𝘀 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. We’ve seen this pattern before. Electricity was once a novelty. The internet was once optional. Today, both are invisible—but essential. AI is following the same path. 👉 From visible tools 👉 To invisible infrastructure And this isn’t theoretical. AI is now driving massive global infrastructure buildouts— especially in data centers, energy, and networks. In fact, AI demand is expected to reshape data center capacity dramatically this decade (McKinsey & Company) This tells us something deeper: 👉 AI is not just software 👉 It is becoming physical infrastructure Because infrastructure doesn’t just support systems— It defines what systems can do. My sense is: 👉 The biggest companies of the next decade will not just build AI 👉 They will build AI-native infrastructure The question is: 👉 Who is building for that future today? Read more: https://lnkd.in/g3mqa-aW #AI #Infrastructure #FutureOfWork #Technology #Strateg
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NetApp argues that as AI moves from pilots to production, the primary constraint is not compute capacity, but data readiness. While GPU shortages dominate industry discussion, many organisations face bottlenecks in data movement, governance and storage efficiency. Our editor-at-large Leona (ลีโอน่า) Lo reports from Singapore where the company recently held INSIGHT Xtra Singapore 2026. Henry Kho Dhruv Dhumatkar #AI #scale #production https://lnkd.in/gUmHXUK9
