Dear Readers,

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Two days ago, NVIDIA reported $81.6 billion in quarterly revenue, with data center sales alone hitting $75.2 billion, up 92% year over year. Those numbers are so large they almost lose their meaning. To put them in perspective: NVIDIA's data center business now generates more revenue in a single quarter than most Fortune 500 companies produce in an entire year. The AI chip market has become one of the most consequential economic arenas on the planet, determining who can train the next frontier model, who can serve billions of inference requests, and ultimately, who controls the infrastructure layer of artificial intelligence itself.

But beneath the headline dominance, something more interesting is happening. The market is fragmenting. Google has split its latest TPU generation into two separate chips for the first time, one for training, one for inference. AMD is shipping competitive hardware and building rack-scale systems that directly challenge NVIDIA's architecture. Cerebras and Groq have demonstrated that specialized silicon can outperform general-purpose GPUs for specific workloads by an order of magnitude. And in China, Huawei is assembling a parallel compute ecosystem that operates entirely outside the Western supply chain, with DeepSeek's V4 model now running natively on Chinese chips.

The question worth examining is whether NVIDIA's position as the undisputed platform of AI compute will hold as the market matures, or whether the shift from training to inference, the rise of vertical integration, and the geopolitical fracturing of the semiconductor supply chain will produce a fundamentally different competitive landscape.

All the best,

Kim Isenberg

In Today's Issue:

Understanding NVIDIA's dominance requires looking beyond raw compute performance. The company's real advantage is systemic. CUDA, the programming framework introduced in 2006, has accumulated roughly four million developers worldwide. Every major AI lab, from OpenAI to Anthropic to Meta AI, builds on CUDA. The libraries, the debugging tools, the kernel optimizations, the deployment pipelines: they all assume NVIDIA hardware. Switching costs are not just financial but organizational. Migrating away from CUDA means rewriting code, retraining teams, and accepting months of reduced productivity.

On top of this software moat, NVIDIA has built what analysts increasingly call a "copper moat," the proprietary NVLink interconnect system that connects GPUs within rack-scale systems at bandwidths far exceeding any external networking solution. The latest Blackwell 300 and upcoming Vera Rubin platforms sell not as individual chips but as integrated AI factories: dozens of GPUs, custom CPUs, liquid cooling, high-bandwidth memory pools, and networking fabric bundled into a single purchasable unit. For customers building large training clusters, this integration eliminates enormous amounts of engineering work.

The financial results reflect this. NVIDIA's fiscal 2026 revenue reached $215.9 billion, with $193.7 billion from the data center segment alone, a 68% increase year over year (NVIDIA, 02/25/2026). The company's Q2 FY2027 guidance of $91 billion suggests the trajectory has not slowed. Gross margins remain near 75%, indicating that despite increasing competition, NVIDIA retains substantial pricing power (SEC Filing, 05/20/2026).

The roadmap underscores the strategy. Blackwell Ultra ships this year, Vera Rubin follows in the second half of 2026 with HBM4 memory and a new CPU architecture, and Rubin Ultra arrives in 2027 with four GPU dies per package and up to one terabyte of HBM4e. NVIDIA has deliberately shifted to a one-year product cadence, which creates a structural problem for competitors: by the time a rival ships a chip designed to match Blackwell, NVIDIA has already moved on to Rubin.

Total available computing capacity from AI chips across all major designers has grown by approximately 3.3x per year since 2022. NVIDIA chips currently account for over 60% of total compute. (Source: Epoch AI, CC-BY)