Unlocking 100 GW of Untapped Grid Capacity with AI

This is exactly the kind of systems-level thinking the AI era demands – treating AI factories not as passive loads but as intelligent grid assets that can actually strengthen the infrastructure they depend on. The combination of NVIDIA's Vera Rubin DSX reference design with DSX Flex and Emerald AI's Conductor platform brings compute, power networking, and control into a single architecture. What's particularly compelling is the potential to unlock up to 100 GW of capacity across the U.S. power system by improving utilisation of existing infrastructure and reducing the need for extensive grid expansion. The field test already demonstrated a 25% immediate reduction in energy consumption – and that's just the beginning. What often gets overlooked in these breakthrough announcements, however, is the physical reality that makes it all possible. Every AI factory – every server rack, every cooling system, every power distribution unit – depends on thousands of electronic components working reliably together: power management ICs, high-speed interface controllers, thermal sensors, protection devices, and specialised logic. Many of these parts are already facing allocation, extended lead times, or outright obsolescence as the industry scales at an unprecedented pace. When a single critical component becomes unavailable, the entire deployment can stall – regardless of how intelligent the orchestration software is. That's precisely where my team and I focus our efforts. We help procurement professionals and engineers secure hard‑to‑find, EOL, and long‑lead‑time electronic components so that AI infrastructure stays on track – from new builds to maintenance and upgrades. If you're building or operating AI infrastructure and ever hit a sourcing wall for critical components, we're here to help. No strings attached – just a partner who understands that even the smartest AI factory is only as reliable as the components it's built on. ⚡ #NVIDIA #EmeraldAI #VeraRubin #DSXFlex #AIFactory #PowerFlexible #EnergyGrid #SemiconductorSupplyChain #HardToFindComponents #AIInfrastructure

Do your self a favor and spend half a day exploring what you can build with Metropolis skills and coding agents. I can promise you that your perspective of what is possible will change.

This is a strong example of where AI agents are moving next. The important part is not just that NVIDIA is offering vision AI agent skills. The important part is what it means for businesses: AI is moving from simple chat support into operational systems that can support inspection, monitoring, reporting and real-time decision-making. But the same rule still applies. Before adopting this kind of technology, businesses need to understand: What problem are we solving? What process will it support? What data will it use? Who reviews the output? What happens if the system gets it wrong? AI agents can create serious value. But only when the business process is clear first. Where do you think vision AI agents will be most useful: manufacturing, security, logistics or healthcare? #ArtificialIntelligence #AIAgents #DigitalTransformation #NVIDIA #BusinessTechnology

It's exciting to see DeepHow and Yazaki Corporation North America included among the companies bringing NVIDIA Metropolis and vision agents into physical operations. Automating time studies is just the start.

Building a production-ready vision AI agent no longer takes thousands of developer hours. NVIDIA Metropolis offers 80+ open agent skills for generating synthetic data, fine-tuning models, deploying applications and unlocking real-time insights, all through natural-language prompts. Leading companies like Fujitsu, Hitachi, OMRON Group, Yazaki North America, and Shimizu Corporation are using Metropolis to bring agentic vision AI into manufacturing and critical infrastructure. https://nvda.ws/4poKGNM

💥 Absolutely fascinating dynamics in the #physicalAI space. 📰 Market Intel ALERT: Applied Intuition Autonomous machines were once defined by their hardware, but as more of their value and IP moves into operating systems, data and AI, the real innovator may be the company that determines what the machine can perceive, decide and do. Applied Intuition appears well positioned here. ⬇️ Bilal Zuberi Mamoon Hamid Nadia Cochinwala Erik Torenberg Paul Kwan #Venturecapital

SK Hynix didn’t “win AI” overnight — it earned its place by doing something unglamorous: building the right memory at the right time. A few years back, most people outside the semiconductor world wouldn’t have been able to tell you what HBM (High Bandwidth Memory) is. Today, it’s one of the key ingredients behind AI performance — sitting alongside GPUs and quietly determining how fast models can be trained and run. This article argues that SK Hynix’s stock is looking compelling again, largely because: It’s a leading supplier of HBM, a critical component for AI workloads It’s tightly positioned in the AI supply chain (think: memory feeding the compute) Even after a strong run, the valuation gap vs rivals has narrowed — but the AI demand story hasn’t gone away Educational takeaway (simple version): AI isn’t just a “chip story”. It’s an ecosystem story. GPUs get the headlines, but memory bandwidth is one of the real bottlenecks — and companies solving bottlenecks often end up owning the next wave. If you’re tracking AI trends, watch the “picks and shovels” businesses: memory, networking, power, and cooling. That’s where the quiet winners often live. What other under-the-radar parts of the AI stack are you watching?

The latest supercomputing rankings are in - NVIDIA is powering 81% of the TOP500 and 89% of all new systems, in addition to: 2x the AI training throughput of all other platforms combined 3x the AI inference throughput The top 8 most energy-efficient systems on the Green500 The world’s AI infrastructure runs on NVIDIA. Read more: #ISC26

🏭Bringing AI to the Factory Floor with Advantech, NVIDIA & Omron Corporation AI is moving into real-world machines — connecting physical data, simulation-first deployment, and intelligent factory operations. Together, Advantech, @NVIDIA, and @Omron Corporation are sharing insights on how to leverage AI to accelerate smart manufacturing, reduce TCO, and drive ROI. ➜Learn more about Edge AI solutions: https://lnkd.in/gyEanbgh