Google's TPU Advancements vs NVIDIA's Dominance: Can Google Disrupt NVDA?

• “According to Google Research: The 7th-gen TPU Ironwood delivers 10x performance improvement over the 5th-gen, and plans to achieve a 1000x increase in computing power, storage, and network capabilities within 4-5 years (at the same cost and energy consumption).”
• “Research shows: NVIDIA holds over 80% value share in the AI server market, with the CUDA ecosystem forming a core barrier that is hard to disrupt in the short term.”
• “Data indicates: Google’s TPU shipments are expected to reach 1.5-2 million units in 2025, equivalent to 40%-60% of NVIDIA’s GPUs, but only account for 8-11% of the value share.”

• “Reddit user: Google’s TPU+OCS architecture has advantages in computing infrastructure, but still relies on NVIDIA GPUs (due to flexibility needs); Anthropic signed a million-TPU agreement to validate demand.”
• “Xueqiu user: Google Gemini 3.0’s text-to-image feature refutes the AI bubble theory, but TPUs are only suitable for Google’s closed technology stack and cannot support the CUDA ecosystem.”
• “Xueqiu user: Optimistic about opportunities in the computing supply chain (optical modules LITE, domestic Xuchuang/Shenghong), NAND flash memory, and energy infrastructure/energy storage.”

Google’s TPU advancements showcase technical prowess, but NVIDIA’s CUDA ecosystem and global supply chain dominance remain insurmountable short-term. While Google’s ASIC shipments may surpass NVIDIA’s GPUs in 2026, high conversion costs from CUDA limit mass adoption. Investment opportunities lie in supply chain players (LITE for optical modules) and energy infrastructure to address power bottlenecks. Both companies will coexist as AI demand grows exponentially, with NVIDIA leading in general-purpose AI and Google excelling in specialized workloads.