▶Dylan Patel consistently emphasizes NVIDIA's overwhelming market dominance, attributing it to a superior, integrated stack of hardware, software, and networking that competitors lack. This point is reinforced across multiple podcasts, with claims that NVIDIA runs over 98% of non-Google AI workloads and is accelerating performance improvements far beyond Moore's Law.Apr 2026

▶A recurring point of agreement is the immense capital expenditure by hyperscalers on AI infrastructure, with Patel frequently citing figures in the hundreds of billions annually, and a total supply chain investment approaching $1 trillion. He consistently argues that Wall Street underestimates this spending.

▶Patel consistently identifies physical infrastructure—specifically data center space, power availability, and eventually semiconductor fabrication capacity (ASML EUV tools)—as the ultimate bottleneck to scaling AI compute, superseding the availability of chips themselves.

▶Across discussions, Patel highlights the intense competition and existential risks faced by AI labs like OpenAI and Anthropic, framing their success as being critically dependent on securing massive, multi-gigawatt compute capacity to keep pace with giants like Meta and Google.

▶Patel's view on Google's AI position appears to have evolved. Early claims suggest Google was slow to pivot its data center strategy for AI, but later analysis indicates he has become more bullish, citing Google's aggressive infrastructure investments, external TPU sales, and competitive model performance.Apr 2026

▶There is a nuanced tension in Patel's analysis of the GPU market. While he notes falling rental prices for H100s due to the introduction of Blackwell, he also claims the market is tightening again, suggesting a dynamic and volatile supply-demand balance rather than a simple price decline.

▶Patel's identification of the primary AI bottleneck shifts with the timeframe. In the near term, he points to power and data center availability as the main constraints. However, looking further out to 2028-2029, he predicts the bottleneck will become ASML's production capacity for EUV lithography tools.

▶While generally bearish on NVIDIA's competitors, Patel provides conflicting signals on AMD. He predicts AMD's AI accelerator revenue share will fall, yet also notes that major players like Microsoft and Meta are actively assisting AMD with software development, and that Microsoft successfully deployed GPT models on AMD hardware.