IndiaAI Mission Expansion Needed in Budget 2026-27

"Excellent analysis, Sunil Gupta synergy between the Union Budget’s fiscal roadmap and the AI mission is a game-changer for India's digital economy. It’s encouraging to see a focus on building a robust ecosystem for local innovation. Looking forward to seeing how these policies translate into real-world impact for the tech sector!"

Big move in AI infrastructure: G42 joins forces with Cerebras to unleash 8 exaflops of compute power in India. What does this mean for India’s tech ambitions and the global AI race? Share your take below. **find link in comment** https://lnkd.in/dpCVBVsv

OpenAI just moved its Stargate initiative into India. Partner: Tata Group. TCS building the data centres. Goal: local AI-ready infrastructure. Not as a backup. As a primary strategy. This matters more than most AI news this week. 𝟭. Compute is going regional. The era of running everything through US hyperscalers is ending. AI at scale requires local infrastructure. Every country with enterprise AI ambitions is now building compute. 𝟮. Enterprise AI deals flow through trust anchors. Tata is not just infrastructure. It is the trust bridge into Indian enterprise. When Tata endorses your AI platform, every Fortune 500 equivalent in India listens. 𝟯. The intelligence layer follows the data layer. You cannot train on Indian data from US servers. Regulation, latency, data sovereignty. Local compute means local advantage. OpenAI did not go to India for market share. They went for infrastructure depth. The question for your team: "Where does your AI infrastructure strategy assume the compute will live in 2027?" --- Regional AI infrastructure is the next competitive moat. Are you watching this?

Physical infrastructure cannot keep up with the demand required for modern enterprise AI strategies. Power grids are maxed out. Data center cooling can't keep up. Backup generators are on multi-year procurement cycles. Fiber routes are stretched thin. These are the constraints shaping every AI deployment happening right now. Companies building AI strategies execute too slowly if they do not address infrastructure scarcity. Qumulo CEO Douglas Gourlay has seen architectural inflection points like this before, from the early internet to the shift to cloud. In his latest for the Forbes Business Council, he makes the case that training a model is a one-time event, but reasoning and inference are continuous acts of truth. The winners in 2027 and 2028 will be the ones who solved this problem: getting accurate, consistent data to flow intelligently toward compute, wherever that compute exists. More from Doug: https://bit.ly/4pT3weC

AI has a “GPU problem,” but this VentureBeat piece argues the real bottleneck is data delivery—getting the right data to accelerators fast enough and efficiently. If you’re building or scaling AI systems, it’s a useful reframing that connects infrastructure choices (storage, networking, pipelines) directly to model performance and cost. Worth a read:

AI isn’t just scaling compute. As we look into the next few years, it’s determining where compute lives, and exposing how unprepared our infrastructure really is. For the last decade, hyperscale meant consolidation: bigger campuses, denser regions, centralized capacity. Inference is reversing part of that trend. Training may stay centralized. But inference is pushing back toward the edge: • Lower latency requirements • Data sovereignty constraints • Power availability bottlenecks • Real estate limitations in core markets The problem? Most edge and regional facilities were designed for yesterday’s thermal loads. You can’t drop 80-150kW racks into infrastructure built for 10–20kW and call it “AI-ready.” Cooling is no longer a mechanical afterthought. It’s becoming the primary constraint on deployment speed. If AI compute is redistributing, liquid cooling has to redistribute with it, at facility scale, not as a bolt-on fix. We unpack this shift and what it means for operators here: https://lnkd.in/g52GX6vG Curious how others are thinking about edge + liquid integration over the next 24 months.

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

Neoclouds capture growing AI workload traffic, Backblaze says: The provider saw sustained traffic growth to GPU specialists in Q4, signaling an ecosystem opportunity beyond hyperscale platforms. #CIODive #Innovation

𝗪𝗗 𝗨𝗻𝘃𝗲𝗶𝗹𝘀 𝟏𝟎𝟎𝗧𝗕+ 𝗛𝗗𝗗 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗔𝗜 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 🛰️ [BUSINESS] Western Digital plans 100TB+ HDDs and performance innovations for AI-driven storage, targeting lower costs and faster access. Why it matters: WD's roadmap addresses the growing storage demands of AI workloads, offering a cost-effective alternative to flash storage. These innovations could significantly impact how organizations manage and access large datasets for AI applications. 🤔 Will HDD innovations be able to keep pace with the rapidly evolving storage demands of AI, or will flash storage ultimately dominate? #HDD #AIStorage #WesternDigital #HAMR #DataStorage 📡 Follow DailyAIWire for autonomous AI news 🔗 https://lnkd.in/demvCeJa

As AI transitions from pilot projects to full-scale production, the real constraint is data readiness. While industry conversations continue to focus on GPU shortages, many organisations are encountering more persistent bottlenecks in data movement, governance frameworks and storage efficiency. Scaling AI requires not just infrastructure, but a coherent data strategy. NetApp’s data-driven push to power ASEAN’s production AI ecosystem addresses these structural challenges. Read more here: https://lnkd.in/gc5aJYrn Thank you to Sneha Naidu for the invitation to INSIGHT Xtra Singapore. I also appreciated the perspectives shared by Henry Kho and Dhruv Dhumatkar on what it takes to operationalise AI at scale across the region. Special thanks to Cat Yong, Enterprise IT News, for driving the story forward. #ArtificialIntelligence #EnterpriseIT #DataStrategy #AIatScale