Nvidia's Growing Influence in AI

Nvidia’s dominance today isn’t just about the H100 chip — it’s the result of multi-decade platform engineering across hardware, software frameworks, and tight integration with the future of AI workloads. They systematically built and continue to defend that edge: 1️⃣ CUDA Lock-In at the Developer Level Today, every major deep learning framework — TensorFlow, PyTorch, JAX — is deeply optimized for CUDA, creating enormous inertia against switching. 2️⃣ Vertical Integration from Silicon to Cloud DGX systems (bundling H100s, NVLink, and Mellanox networking) offer full-stack optimization. Nvidia controls not just training chips, but high-bandwidth interconnects, model parallelism frameworks, and enterprise-ready AI infrastructure (DGX Cloud). 3️⃣ AI Workload-Specific Optimization Hopper was tuned for transformer models — custom Tensor Cores, FP8 precision, sparsity support — years before general-purpose chips adapted. Architecture decisions at Nvidia are increasingly model-first, not architecture-first. 4️⃣ Own the Inference Stack Too TensorRT and Triton Inference Server form a production-grade deployment layer, optimizing models post-training for latency, throughput, and cost — critical as AI workloads shift to inference at scale. 5️⃣ Closed-Loop Research Collaboration Unlike commodity chipmakers, Nvidia co-engineers future architectures with hyperscalers (e.g., OpenAI, DeepMind, Meta AI) before models are published. This feedback loop compresses iteration cycles and keeps Nvidia tuned to upcoming workload demands 12–24 months ahead. 6️⃣ Ecosystem Expansion into Vertical AI Domains Frameworks like Omniverse (simulations), Isaac (robotics), and Clara (healthcare AI) position Nvidia to dominate not just AI infrastructure, but domain-specific AI applications. 🏁 I still wonder whether Nvidia’s valuation is truly stretched — or simply a glimpse of a much bigger future.

Nvidia's earnings report is so much more than that. It's the ✨State of the AI Union✨, a quarterly check-in on the global replatforming of the economy. In 1Q26, NVIDIA lost access to the $50B China market, wrote off $4.5B in unsellable chips, and still grew revenue 69% year-over-year to $44B. The stock soared. Jensen smiled. Somewhere, an analyst updated their AI TAM model. Again. My key takeaways: 🪓 China decoupling. U.S. banned H20 chip exports mid-quarter. No grace period, no warning. One day they’re legal, the next they’re geopolitical contraband. Nvidia ate a $4.5B loss like it was a light afternoon snack - and still posted record growth. NVIDIA’s non-China demand engine is strong enough to sustain growth even with one of its largest markets cut off. The message to Washington: export controls aren’t limiting China. They’re limiting U.S. platforms: "The question is not whether China will have AI, it already does. The question is whether one of the world's largest AI markets will run on American platforms." 🏭 Sovereign AI = New Growth Engine. Countries are racing to build national AI platforms.Nvidia has a line of sight to projects requiring “tens of gigawatts of NVIDIA infrastructure” with “nearly 100 AI factories in flight, 2x YoY”. These deployments now sit in the same conversations as national energy policy and foreign diplomacy. In the past, you’d ask who has oil. Today, you ask: who’s on the GB200 rack list? 🧠 Inference is exploding. Models are no longer just parroting back one-shot answers. They’re reasoning, planning, thinking. That requires significantly more compute. In fact, Microsoft processed over 100 trillion tokens in Q1, +5x YoY. 🌐 Networking and Full-Stack Control Is the Moat. NVIDIA’s networking stack - NVLink for scale-up, Spectrum-X for scale-out - is how it locks in the entire AI lifecycle.  Spectrum-X is now annualizing at $8B. NVLink shipments exceeded $1B this quarter. RTX, DGX, Blackwell, NeMo, Omniverse - call it what you want. Jensen built a full-stack digital deity complex, quietly absorbing workloads across consumer, enterprise, industrial, and national layers. To wrap up the call, Jensen Huang listed four “positive surprises” that are now structural tailwinds: 1️⃣ Reasoning AI is real, and expensive 2️⃣ The AI Diffusion Rule got rolled back, meaning the U.S. now wants to export the American stack, not just hoard it 3️⃣ Enterprise AI is working in production 4️⃣ Industrial AI is back, and this time it’s doing factory planning, not just arm-waving demos Each of these trends requires enormous amounts of compute, which is convenient, because NVIDIA has some to sell. You can describe NVIDIA as a chipmaker. Or a systems provider. Or a vertically integrated planetary cognition pipeline. But the simplest way is this: They sell the hardware that turns electricity into intelligence. And increasingly, the world is willing to pay for that - by the rack, by the token, by the petaflop.

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.