国泰海通证券研究·2025-12-03 13:47Core Insights - The article emphasizes that the pre-training Scaling Law remains valid, with Google's Gemini demonstrating significant advancements in AI capabilities, particularly in multi-modal reasoning and user data integration, which strengthens its competitive edge in the AI ecosystem [1][2]. Group 1: Model and Technology - Gemini has optimized multi-modal capabilities, achieving a screen understanding score of 72.7% in the ScreenSpot-Pro test, significantly outperforming competitors like GPT-5.1 (3.5%) and Claude Sonnet (36.2%), indicating its potential for GUI operations [2]. - The advancements in Gemini are attributed to substantial investments in pre-training, validating the effectiveness of Google's approach to AI development [1]. Group 2: TPU Ecosystem - Google has accelerated the optimization of its TPU ecosystem, enhancing external usability by supporting PyTorch and investing in open inference ecosystems, which improves TPU's market competitiveness [3]. - TPUv7 shows a Total Cost of Ownership (TCO) advantage, being 44% lower than GB200 servers for internal use and 30% lower for external leasing compared to GB200, and 41% lower than GB300 [3]. - The TPU's role is seen as crucial for building a comprehensive AI ecosystem rather than merely selling the hardware, aiming for optimal cost and efficiency in cloud services [3]. Group 3: Competitive Landscape - The long-term competitive landscape suggests that TPU is unlikely to completely disrupt NVIDIA's GPU dominance but may serve as a complementary solution for specific customer segments [3]. - NVIDIA's established supply chain advantages and the appeal of its GPU's out-of-the-box usability for small to medium customers present challenges for TPU's market penetration [3].