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

• “据谷歌研究：第七代TPU Ironwood性能较第五代提升10倍，计划4-5年内实现算力、存储和网络能力1000倍提升（相同成本能耗）”
• “研究显示：NVIDIA在AI服务器市场价值占比超80%，CUDA生态系统形成核心壁垒，短期内难以被颠覆”
• “数据表明：2025年谷歌TPU出货量预计150-200万片，相当于NVIDIA GPU的40%-60%，但价值占比仅8-11%”

• “Reddit用户：谷歌TPU+OCS架构在算力基建具优势，但仍依赖NVIDIA GPU（灵活性需求）；Anthropic签百万TPU协议验证需求”
• “雪球用户：谷歌Gemini3.0的文生图功能反驳AI泡沫论，但TPU仅适用于谷歌封闭技术栈，无法支持CUDA生态”
• “雪球用户：看好算力供应链（光模块LITE、国内旭创/胜宏）、NAND闪存及能源基建/储能机会”

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出货量 may surpass NVIDIA’s GPU in 2026, high conversion costs from CUDA limit mass adoption. Investment opportunities lie in supply chain players (LITE for光模块) 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.