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NVIDIA's revenue growth is speeding up even as its revenue base expands massively, a rare feat that defies the "law of large numbers." This suggests strong network effects and a dominant market position are creating a self-reinforcing cycle of demand for its AI hardware.

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The strongest evidence that corporate AI spending is generating real ROI is that major tech companies are not just re-ordering NVIDIA's chips, but accelerating those orders quarter over quarter. This sustained, growing demand from repeat customers validates the AI trend as a durable boom.

AI companies are achieving revenue milestones at an unprecedented rate. Data shows AI labs growing from $1B to $10B in revenue in roughly one year, a feat that took Salesforce 8-9 years. This signals a dramatic acceleration in market adoption and value creation.

A single year of Nvidia's revenue is greater than the last 25 years of R&D and capex from the top five semiconductor equipment companies combined. This suggests a massive 'capex overhang,' meaning the primary bottleneck for AI compute isn't the ability to build fabs, but the financial arrangements to de-risk their construction.

Despite bubble fears, Nvidia’s record earnings signal a virtuous cycle. The real long-term growth is not just from model training but from the coming explosion in inference demand required for AI agents, robotics, and multimodal AI integrated into every device and application.

While known for its GPUs, NVIDIA's true competitive moat is CUDA, a free software platform that made its hardware accessible for diverse applications like research and AI. This created a powerful network effect and stickiness that competitors struggled to replicate, making NVIDIA more of a software company than observers realize.

The exponential growth in AI required moving beyond single GPUs. Mellanox's interconnect technology was critical for scaling to thousands of GPUs, effectively turning the entire data center into a single, high-performance computer and solving the post-Moore's Law scaling challenge.

The current wave of AI companies is growing at unprecedented rates, far outpacing the growth curves of the mobile, social, or SaaS eras. They are becoming larger and more consequential much faster, a phenomenon described as "speed running the process of company growth."

The debate on whether AI can reach $1T in revenue is misguided; it's already reality. Core services from hyperscalers like TikTok, Meta, and Google have recently shifted from CPUs to AI on GPUs. Their entire revenue base is now AI-driven, meaning future growth is purely incremental.

In five years, NVIDIA may still command over 50% of AI chip revenue while shipping a minority of total chips. Its powerful brand will allow it to charge premium prices that few competitors can match, maintaining financial dominance even as the market diversifies with lower-cost alternatives.

AI's computational needs are not just from initial training. They compound exponentially due to post-training (reinforcement learning) and inference (multi-step reasoning), creating a much larger demand profile than previously understood and driving a billion-X increase in compute.