作者 | 高允毅 2025 年底，大模型行业的"年终决战"正式打响，各家纷纷亮出压箱底的杀手锏，就在这场激烈角逐中，Gemini 3 以绝对王者 之姿强势突围，一登场就刷新了行业的认知边界。 11 月 18 日，Gemini 3 直接"横扫"多项权威基准测试，以"世界最强多模态理解""交互最深智能体""推理怪兽"的姿态，强势碾压 全球所有同类模型。谷歌 CEO 桑达尔·皮查伊亲自为其站台，直言这是"迄今为止最智能的模型"。消息一出，整个 AI 圈瞬间沸 腾，所有人都在追问：Gemini 3 的强悍，到底藏着什么秘诀？ 答案在发布当天就有了初步线索。Google DeepMind 研究与深度学习副总裁 Oriol Vinyals 直接在推特上"剧透"： "Gemini 3 这么强，核心秘诀就两点：更好的预训练，更好的后训练。 "这番直白的表态，让"预训练"与"后训练"瞬间成为行业热议的核心 话题。 作为从强化学习转向表征学习的资深研究者，Sebastian Borgeaud 的预训练功底堪称深厚：从 Transformer 架构，到 BERT、 XLNet，再到 DeepMind 第一篇大语言模型论文 Goph ...

报告来源 以上内容节选自国泰海通证券已发布的证券研究报告。 报告名称： Gemini 3、TPU、端侧AI应用更新报告——模型多模态升级加速端侧AI落地，TPU冲击算力 格局；报告日期：2025.12.02 报告作者： 秦和平（分析师），登记编号：S0880523110003 刁云鹏（研究助理），登记编号：S0880125070016 重要提醒 本订阅号所载内容仅面向国泰海通证券研究服务签约客户。因本资料暂时无法设置访问限制，根据《证 券期货投资者适当性管理办法》的要求，若您并非国泰海通证券研究服务签约客户，为保证服务质量、 控制投资风险，还请取消关注，请勿订阅、接收或使用本订阅号中的任何信息。我们对由此给您造成的 不便表示诚挚歉意，非常感谢您的理解与配合！如有任何疑问，敬请按照文末联系方式与我们联系。 报告导读： 模型：预训练 Scaling Law 仍然成立；算力： TPU 助谷歌构建全栈 AI 生 态，长期或与英伟达 GPU 互补；应用：多模态推理能力为端侧 GUI 操控提供可能，豆包 手机助手率先落地，看好谷歌全栈集成、苹果系统掌控、阿里模型能力。 Gemini 验证了预训练 Scaling Law ...

Group 1: Global AI Outlook - The report highlights a new paradigm in AI development characterized by new scaling, new architecture, and new total addressable market (TAM) opportunities [1] - The demand for computing power is expected to rise due to advancements in both training and inference processes, potentially unlocking new TAMs [1][3] - The report maintains a positive outlook on AI industry investments, anticipating that global AI applications will enter a performance harvesting phase [1] Group 2: Model Development - The pre-training scaling law is anticipated to open a new starting point for model development, with significant innovations in architecture being explored [2][23] - The report notes that the classic transformer architecture has reached a parameter scale bottleneck, with existing public data nearly exhausted [2][20] - Major tech companies are experimenting with new architectures, such as Tencent's Hunyuan TurboS and Google's Gemini Diffusion, which may accelerate scaling law advancements [23][24] Group 3: Computing Power Demand - The report identifies a clear long-term upward trend in computing power demand, driven by both training and inference needs [3][32] - New scaling paths are emerging in the post-training phase, with ongoing exploration of new architectures that may reignite pre-training demand narratives [3][33] - The deployment of large-scale computing clusters, such as OpenAI's StarGate, is expected to support the exploration of pre-training [38] Group 4: Application Development - The report indicates that the rapid advancement of agent applications is leading to a performance harvesting phase for global AI applications [4][67] - The commercialization of agent products is accelerating, with domestic AI applications quickly iterating and entering the market [4][67] - The report emphasizes that agent applications are evolving from simple tools to complex solutions, with significant growth expected in various sectors [5][68] Group 5: Business Model Transformation - The shift from traditional software delivery to outcome-based delivery is highlighted as a key trend, with quantifiable ROI accelerating the adoption of agent applications [5] - Specific sectors such as consumer-facing scenarios (advertising, e-commerce) and AI in marketing/sales are expected to lead in commercialization due to their inherent advantages [5][67] - The report notes that AI applications in HR are transitioning from efficiency tools to strategic hubs, indicating a broader transformation in business models [5][67]