Yi-Min Wang

Jensen Huang dropped a number at #GTC 2026 that stopped me mid-scroll. "I believe computing demand has increased by 1 million times over the last two years." Jensen Huang, GTC 2026 Not over a decade. Not someday. Two years. Then came the slide that reframed everything for me. The entire AI infrastructure thesis in one frame: ▸ AI Factories are the Industrial Infrastructure of the AI Era ▸ Inference is the Workload ▸ Tokens are the New Commodity ▸ Compute is Revenue And the line Jensen said out loud: "It's now a factory to generate tokens." on the All-In podcast at GTC, Jensen laid out a thought experiment. If a $500K/year developer spent less than $250K on tokens by year end, he'd be "deeply alarmed." My read on that: it's not a pricing benchmark. It's a provocation. It's Jensen saying we are still dramatically underestimating how much value a token can unlock. Here's where I land on all of this. The cost of generating tokens is dropping fast. The value of what those tokens can do is rising exponentially. The gap between those two curves? That's where the real opportunity lives. But and this is the part most people skip the gap only gets captured if the infrastructure beneath it is built right. #Tokens don't generate themselves. #Inference doesn't optimize itself. And enterprises won't run mission-critical agentic workloads on infrastructure that can't guarantee latency, throughput and cost-per-token at scale. Whoever builds that layer , reliably, efficiently and at enterprise grade #owns the #AIstack. The inference delivery platform. At NeevCloud®, this is exactly what we're building toward on our AI SuperCloud platform. Because Jensen is right the AI factory era is here. The question is who builds the pipes that make the factory run. What's your take is token cost the most underloved problem in enterprise AI right now? Drop your thoughts below 👇 Note: I didn't attend GTC in person. Insights are from the official NVIDIA GTC keynote on YouTube and public coverage. Screenshot credit: NVIDIA GTC 2026 official livestream. #NvidiaGTC #AIInference #TokenEconomy #AgenticAI #EnterpriseAI #AISuperCloud #NeevCloud

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Huge news in open AI research: the Allen Institute for AI just secured $152M from the NSF and NVIDIA to lead the Open Multimodal AI Infrastructure to Accelerate Science (OMAI) project. This effort will create open, fully reproducible AI models that integrate text, images, code, and more—accelerating breakthroughs in fields from biology to energy. It’s a big step toward multimodal AI that can bridge the digital and physical worlds, potentially enabling future systems that don’t just reason about data, but generate insights, run experiments, and interact with real-world systems. Open, collaborative, and built to advance science for everyone—this is the kind of foundation that could power the next wave in physical AI!

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GTC 25, Washinton D.C.: NVIDIA CEO Jensen Huang underscored NVIDIA's partnership with Palantir saying Palantir Ontology is the "single most important enterprise stack in the world today." The collaboration is focused on government, national security, and global enterprises. Accelerating data processing "at a much larger scale and speed" is critical for time-sensitive use cases like: * Real-time predictive maintenance for critical infrastructure * Rapid supply chain disruption response * High-volume fraud detection and anomaly mapping * Operational intelligence and decision-making in defense https://lnkd.in/g9rTR23e

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I wrote an article about a build where an AI agent uses LangGraph + LangSmith with the Azure OpenAI Service to gather the latest updates from Azure, OpenAI, Google, AWS, and Meta, and automatically turn them into a PDF report. Perfect for organizing the nonstop AI updates—hope it helps! 🚀 #AzPoC🚀 https://lnkd.in/gPCnX6De

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Excited to see real-world proof of ARM’s impact on streaming. This collaboration between Amazon Web Services (AWS), Playtech, and the Wowza team makes this a must-watch for anyone focused on streaming performance and cost.

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The Brookings Institution has identified five distinct AI clusters in US metros: 🌟 Superstars 🌟 Star Hubs 🌟 Emerging Centers 🌟 Focused Movers 🌟 Nascent Adopters Standing out as an AI Star Hub, the Phoenix-Mesa-Chandler metro area ranks 17th nationally. 🚀 Anticipating positive spillover effects from TSMC's expansion in Arizona, benefiting both the semiconductor ecosystem and AI sector. #AI #Innovation #aidatacenters #semiconductors State of Arizona City of Chandler, Arizona City of Phoenix City of Mesa City of Tempe

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If you want to understand what an MCP server is and how to build one quickly, follow this quick tutorial. #MCP #AI https://lnkd.in/gPfJsC8P

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The AI Research Team at Cisco Central AI Org keeps innovating how LLMs can be applied to help customers in managing large complex operational problems. This blog that the team just published is a great example of addressing the 'Large Context Window' bottleneck, delivering a scalable framework that slashes LLM latency and operational costs without compromising precision. https://lnkd.in/giFG45Cx

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