AI for Building Core Technology Infrastructure

NVIDIA GTC 2025 Recap — Through My Lens Jensen Huang just turned NVIDIA GTC into the Super Bowl of AI, and the announcements this year were nothing short of enterprise-defining. Here’s what stood out — especially for those of us thinking about IT infrastructure, scaling AI, and business impact. 1)    Tokens are the new currency of computing Instead of retrieving data, future data centers will generate tokens — representing reasoning, ideas, and foresight. Think: music, code, applications — all powered by token generation. 2)    Data centers are transforming into AI factories By 2028, AI infrastructure buildout could hit $1 trillion.  Blackwell chips are a key part of that — now in full production. Jensen said it loud: “We need 100x more compute than we thought.” This is NVIDIA's Thinking process around scaling AI  1. Solve the data problem 2. Solve the training problem 3. Solve the scaling problem KEY PARTNERSHIPS:   If you look at one of the images I have attached, you will see that NVIDIA has a partnership with almost every company in the IT Space.  From a guy who has been in this space for more than a decade, this is insane.  However, lets look at the few ones that will have the most impact. • Cisco + NVIDIA + T-Mobile: Full-stack edge AI infrastructure with Edge 6G in focus — think latency-free, real-time intelligence at the edge. Huge for IT leaders planning long-term architecture. • Cisco Hyperfabric AI: Cloud-managed infrastructure. Design AI clusters online. Plug-and-play. “Helping hands” validate design — wiring, agents, compute — all the way through. • NVIDIA + Nutanix: Enterprise-ready AI infrastructure stack. Secure, scalable. Accelerates adoption in traditional data center environments. • NVIDIA + GM: Building the future of autonomous vehicles. Includes NV Halos – chip-to-deployment AV safety system, already 7M miles safety assessed. AI IS CREATING AI NOW • NVIDIA Dynamo: Open-source OS for agentic AI. • The future isn’t just running models — it’s building agents. FINAL THOUGHTS We’re not just watching the future unfold — we’re standing in it. NVIDIA’s stack is rewriting how we think about infrastructure, energy, applications, and computing. They have a roadmap for the next 3 years to 25x compute from where they are today.  The businesses that prepare now—with the right partners, architecture, and strategy—will lead.

🚨Deloitte has launched a global AI Infrastructure Center of Excellence (CoE), aiming to support clients in designing and running specialized AI data centers. The move comes as enterprises increasingly recognize that scalable, secure, and efficient infrastructure is no longer optional, it's foundational to deploying AI at speed and scale. The CoE sits at the heart of Deloitte’s Silicon2Service AI factory-as-a-service model, linking foundational technology (like silicon and GPUs) to enterprise-ready AI outcomes. By focusing on infrastructure as a strategic enabler, not just a backend function. Deloitte is doubling down on helping organizations move from AI experimentation to fully operational, enterprise-grade deployment. From digital twins and GPU-intensive workloads to cyber-resilient data centers, the CoE brings together advanced capabilities that few firms can offer under one roof. Its emphasis on performance, governance, and adaptability reflects a growing industry consensus: AI infrastructure is not just a technical concern, but a critical competitive lever. What’s interesting is the shift in how firms like Deloitte are positioning infrastructure, less as a cost center and more as a driver of transformation and innovation. Will more enterprises start treating infrastructure as a board-level priority? And could this redefine how we measure readiness for AI at scale? #ArtificialIntelligence #AIEcosystem #AIInfrastructure #PostCloud #DataCenters #DigitalTransformation #Deloitte #TechStrategy #MLOps #PostMergerIntegration #CarveOut #AIatScale #FutureOfWork #PMI #SmartInfrastructure #TechLeadership #EnterpriseAI

20 years ago Mary Meeker called the internet’s rise; 10 years ago, the mobile revolution. Last week, she made her biggest bet yet... And it has nothing to do with models. In her new 340-page report, Meeker reveals what’s actually driving AI’s future: Infrastructure. Not just chips. But power, land, CapEx, and velocity. Here are the 10 most important takeaways from her report ranked from most to least significant: 1. CapEx is now strategy. $455B in 2024 AI data center spend. A 63% YoY jump. Not a spike, this is a structural shift. Infrastructure is the product. 2. Power is the gating item. Data centers use ~1.5% of global electricity. Demand is growing 4x faster than grid supply. The bottleneck is the grid. 3. Inference eats the future. Training is episodic. Inference is forever. As AI agents scale, inference will drive long-term infra costs. 4. Speed is a strategic moat. xAI built a 750,000 sq. ft. facility in 122 days. Deployed 200K GPUs in 7 months. Fast build = competitive edge. 5. Custom chips = stack control. Amazon (#Trainium), Google (#TPU), Microsoft (#Phi). Silicon is no longer optional, it’s leverage. 6. Overbuild is intentional. Hyperscalers are doing what Amazon Web Services (AWS) did in 2006: build ahead of demand. Surplus compute becomes a platform. 7. Emerging markets are the next frontier. 50% of internet users. <10% of AI infra. With the right energy and capital stack, emerging markets could leapfrog legacy hubs. 8. AI data centers are AI factories. "Apply energy, get intelligence." - Jensen Huang, NVIDIA CEO. Not metaphor. New economics. 9. Cooling and grid tie-ins are the edge. Latency, liquid cooling, substation access, infra is no longer just real estate. It’s engineering. 10. Sovereignty is back. Governments are co-investing in “Sovereign AI.” Infra is no longer neutral, it’s strategic. The next wave of AI winners won’t be those with the smartest models. They’ll be the ones who control the stack those models run on. #datacenters