行业看好各头部大厂凭借自身生态积淀打造AI 超级入口。 AI赛道的"流量争夺战"进入白热化阶段。其中，腾讯持续发力，为元宝大额买量投放；阿里巴巴"千问"后发制人，增长势头迅猛。据第三方机构DataEye研 究院旗下ADX行业版数据，11月大陆市场头部原生AI APP中，腾讯元宝买量素材领先，千问增长明显，月末日投放素材量甚至反超腾讯元宝。 今日DataEye在报告中披露国内外头部AI原生应用下载量与买量投流两大核心维度数据变化。国内市场中，字节系的两家豆包、即梦AI，以及腾讯元宝、千 问、DeepSeek总下载量位列前五。其中阿里千问表现突出，11月中旬前其前身通义APP日下载量约1万次，中旬后暴涨至15万次左右并维持至月底。 在基础模型训练效率进入瓶颈期的当下，海内外头部模型厂商开始探索新的路径，迈入算力规模扩张与架构优化多场景落地的差异化方向。字节跳动通过豆 包AI手机将AI超级入口的争夺战争挑明后，阿里也向千问投入更多资源。大模型时代的商业竞争，再次回归大厂比拼资源与速度的"轮回"。 千问投放量暴涨 DataEye旗下点点点数据显示，2025年10月至11月大陆市场苹果端头部AI APP下载量前五名中， ...

Core Viewpoint - Nvidia is collaborating with Uber to utilize diverse driving scenario data collected by Uber to enhance the training of Nvidia's Cosmos World foundational model for autonomous driving technology [1][3][4]. Group 1: Collaboration Details - Nvidia Drive announced the partnership with Uber, focusing on leveraging Uber's extensive real-world driving data to accelerate advancements in autonomous driving [3][4]. - The collaboration aims to achieve three main technical objectives: higher precision in simulation, reduced iteration cycles for model training, and improved stability in rare or extreme scenarios [5][9]. Group 2: Data Advantage - Uber's global operational network provides unique data value for training autonomous driving models, with over 1 billion rides and deliveries conducted monthly [8]. - Uber vehicles operate more frequently in complex situations, such as adverse weather and crowded events, which are less common for private vehicles, thus enhancing the data collection for rare scenarios [8][10]. Group 3: Specific Use Cases - For instance, Uber's airport pickup and drop-off services operate under various weather and lighting conditions, making it challenging to sample these scenarios adequately in other contexts [13]. - The unique environment of airport pickups, characterized by dense traffic and unpredictable pedestrian movements, presents specific challenges that can be effectively captured through Uber's data, including driver performance metrics [14].