Google's TPU Advancements vs. NVIDIA: Competitive Dynamics & Investment Implications

• Reported by OSCHINA: Google plans to achieve a 1000-fold improvement in computing power, storage, and network capabilities at the same cost and energy consumption over the next 4-5 years, doubling its computing capacity every 6 months. Its 7th-gen TPU Ironwood single-chip performance is more than 4 times that of v6e, with 192GB HBM and 7.2TB/s bandwidth, and the cluster computing power reaches 42.5 hundred trillion billion operations per second.
• Reported by Segmentfault: Google’s Gemini3 Pro model scored 37.5% in the Humanity’s Last Exam (HLE) test, significantly outperforming GPT5.1’s 26.5% and leading in multiple professional industry evaluations.
• Reported by CITIC Securities: NVIDIA accounts for over 80% of the value share in the AI server market, with AI GPU supply expected to reach 5-6 million units in 2025, far exceeding Google’s TPU (1.5-2 million units) and Amazon’s Trainium2 (1.4-1.5 million units).

• Reddit user: Google’s TPU+OCS architecture has advantages in computing infrastructure, but it still relies on NVIDIA GPUs to meet the flexibility needs of custom operations and mixed workloads; Anthropic’s signing of a 1 million TPU agreement verifies growing demand.
• Xueqiu user: Google’s TPU is a closed technology stack that does not support the CUDA ecosystem, so NVIDIA remains the core choice for third-party developers. Investment opportunities are concentrated in computing supply chains (e.g., LITE, Xuchuang, Shenghong), NAND flash memory (SanDisk), and energy infrastructure (e.g., Bloom Energy), while software companies with low bargaining power face headwinds.

Research and social media align on Google’s technological progress but agree NVIDIA retains irreplaceable value for third-party developers due to its CUDA ecosystem and global supply chain. Contradictions are minimal—both sides recognize Google’s architectural strengths but note its limited applicability outside its own stack. For investors, key opportunities lie in computing supply chains (optical modules like LITE, PCB manufacturers like Xuchuang and Shenghong), NAND flash, and energy infrastructure (addressing power supply bottlenecks). Weaker software firms face headwinds as AI models enable non-technical users to create applications without relying on their services.