Assess AI Infrastructure Maturity and Scalability

AI just got more practical with HPE GreenLake Intelligence. This new framework makes hybrid operations smarter, simpler, and more agentic. Learn more https://lnkd.in/ggA893YQ

People keep arguing about agents vs workflows in AI. But here’s the real question: * Can you deliver it? Can you keep it running? Can users actually use it? * The smartest agent system means nothing if it never leaves your machine. * Distribution > architecture. Every. Single. Time.

Most event tech stacks are optimizing the wrong thing—and AI is making it worse. Not because the tools are bad, but because the decisions are happening too early. This article lays out the shift from automation to decision architecture. ▸ https://lttr.ai/Ams2H #EventTechnology #SystemsThinking #ArtificialIntelligence #AttendeeJourney #Businesstech

What drives the next phase of AI adoption? Cisco's Jeetu Patel, President & Chief Product Officer, spoke with CNBC-TV18 at WEF on how enterprises can move from AI pilots to real-world impact - anchored on secure infrastructure, trust, and readiness at scale. Learn more: https://cs.co/6040h6SC0

Most event tech stacks are optimizing the wrong thing—and AI is making it worse. Not because the tools are bad, but because the decisions are happening too early. This article lays out the shift from automation to decision architecture. ▸ https://lttr.ai/Ams4c #EventTechnology #SystemsThinking #ArtificialIntelligence #AttendeeJourney #Businesstech

As AI adoption accelerates, the conversation often jumps quickly to applications and use cases. In reality, the foundation matters just as much — reliable compute, power, cooling, and long-term infrastructure planning. Without this base, even the best AI models struggle to scale responsibly, especially in public and regulated environments. These are some of the questions I’m spending time thinking about.

Most of today’s ‘AI agents’ aren’t autonomous intelligence. They’re orchestration pipelines: microservices wrapped with LLM-based parsing, routing, and tool execution. EBRM will make many current agent stacks look outdated fast, by improving reward reliability, robustness, and behavioral consistency at the core. Thanks to prof Yann LeCun for continuing to push the boundaries.

Great discussion on the acceleration of AI inference at the Edge and the real opportunities it unlocks. At Submer and InferX AI, we are focused on supporting customers across their AI journey, from infrastructure to production-ready inference. Thank you DatacenterDynamics for the opportunity. Recording here: https://lnkd.in/ew2stQrv

Most enterprises start with basic GPU clusters and wonder why they can't scale. The path from cluster to AI factory has 5 clear stages: 1. Basic Clusters → Manual provisioning, constant firefighting 2. Advanced Scheduling → Better GPU utilization, still fragile 3. Multi-Tenancy → True isolation without overhead 4. Platformization → Self-service that works 5. AI Factory → Automated, policy-driven, audit-ready Each stage solves specific pain: utilization, team velocity, governance, cost control. With Sovereign AI and agentic workflows on the horizon, getting multi-tenancy right now matters more than ever. Stage-by-stage breakdown: https://lnkd.in/dxfScPnQ

Introducing Scalar i7 RAPTOR: the ultimate solution for AI data lakes. Designed for efficiency, density, and cost-effectiveness, it supports hyperscale and exascale environments, empowering organisations to scale AI workloads seamlessly and sustainably. https://okt.to/6Zyadx