以下文章来源于豹变 ，作者张经纬 豹变 . 直抵核心。做最具穿透力、洞察力的商业观察，深度影响未来。 AI应用争夺"全民时刻"。 作者丨张经纬 编辑丨邢昀 来源丨豹变 临近春节，AI应用纷纷计划给用户发钱。 腾讯元宝在2月1日上线春节红包活动，预计共发放10亿元，单个用户最高可获得万元。对此马化腾 表示，"希望重现微信红包的盛况"。 老对手字节豆包则将目光投向了春晚，火山引擎将成为央视春节晚会独家AI云合作伙伴，AI助手豆 包也将配合上线多种互动玩法，其中也包括发红包。 百度文心则双管齐下，1月26日至3月12日，在百度APP使用文心助手，用户会得到总计5亿、最高1 万的现金红包。此外，百度APP也将成为北京电视台春晚的首席AI合作伙伴。 将投中网设为"星标⭐"，第一时间收获最新推送 这熟悉的一幕，仿佛是移动支付与短视频时代春节"撒币拉新"的翻版。巨头们不惜重金，想复刻当年 微信支付"偷袭珍珠港"式的成功，为尚未引爆的AI应用寻找那个关键的"全民时刻"。获取用户心 智，也是为了跑通商业化路径。 然而，在移动互联网用户增长见顶、注意力极度分散的今天，AI的"红包大战"还能烧出之前的故事 吗？ 春节，互联网产品的 ...

江苏 Jiang Su 1月30日，航拍建设中的南京北站枢纽工程。截至目前，南京北站正负零以下结构施工全部完成，项目全面转入地面主体结构施工阶段。 赵亚玲 摄 视觉江苏网供图 股市 Equity Market 1月30日，沪指本月累计涨3.76%，呈现"冲高后窄幅震荡"的走势，一月中旬再创十年新高后小幅回落，最终站稳4100点关口上方。科创50指数表现 较强，月累计上涨超12%。上证50指数走出"先扬后抑"的态势，日线一度收出6连阴。市场交投活跃，沪深两市成交额连续20个交易日突破2.5万亿 元。从板块来看，1月热点聚焦度显著提升，有色金属与科技两大主线交替领涨。 国内 财政部经济建设司副司长吴盖介绍说，据初步统计，2025年消费品以旧换新相关产品销售额超过2.6万亿元，惠及约3.6亿人次，其中汽车以旧换新超 过1150万台，新能源汽车在汽车以旧换新中的销售占比将近60%。 环球 美国电力监管机构北美电力可靠性协会（NERC）发布的一份最新报告显示，未来五年内，多达1.51亿美国人将面临电力短缺甚至全面停电的风险。 主要原因包括极端天气、天然气系统脆弱性以及AI热潮下的电力需求激增。 沙特宣布启动"国家私有化 ...

Core Insights - Major tech companies are launching aggressive marketing campaigns for AI applications during the upcoming Spring Festival, reminiscent of past strategies that successfully attracted users through cash giveaways [2][3][5] - Tencent, ByteDance, and Baidu are among the key players planning to distribute significant amounts of cash, with Tencent's WeChat expected to issue 1 billion yuan in red envelopes, aiming to replicate the success of previous years [2][3] - The competition for user acquisition during the Spring Festival is not merely a marketing tactic but a strategic move to secure a dominant position in the AI market [3][5] Group 1: AI Red Envelope War - Tencent's WeChat is set to launch a red envelope campaign with a total of 1 billion yuan, with individual users eligible for up to 10,000 yuan [2] - ByteDance's Doubao will partner with CCTV for the Spring Festival Gala, introducing various interactive features, including red envelopes [2] - Baidu's Wenxin will distribute a total of 500 million yuan in cash red envelopes from January 26 to March 12, also becoming the chief AI partner for the Beijing TV Spring Festival Gala [2][3] Group 2: Historical Context and Strategic Importance - The Spring Festival has historically been a critical battleground for internet products, with past successes like WeChat's red envelope feature leading to significant user growth [3][5] - The combination of the Spring Festival and the Spring Festival Gala has proven to be a powerful strategy for reshaping user habits and achieving exponential growth [5][7] - The competition during this period is seen as a pivotal moment for AI applications to gain traction and establish a user base [8][11] Group 3: Challenges and Opportunities for Smaller Companies - Smaller AI companies face challenges in competing with the financial might of larger firms during the Spring Festival, leading them to focus on enhancing their technology and user experience [13][14] - Companies like DeepSeek and Kimi are working on improving their models to create differentiated offerings, with DeepSeek expected to launch its next-generation AI model around the Spring Festival [13][14] - The influx of new users attracted by the major companies' red envelope campaigns may ultimately benefit the entire AI industry by increasing overall market demand [14][15]

连板晋级率降至10.53%，连板高度降至5连板的湖南黄金，且3板及以上连板股家数降至仅有2只。随着黄金、白银等金属期货高位大幅回调，引发此前连续 展开逼空的有色板块遭遇资金集中出逃，此前8连板的白银有色（维权）全天封死一字跌停，带动豫光金铅、招金黄金等30余只成分股遭遇跌停。随着资源 股做空动能集中释放，市场风偏急剧下降并向农业等防御性板块聚集，此外趋势抱团资金主导的算力硬件股也重拾升势，成为创业板指展开深V反转主因。 随着年报强制预告期结束，关注一些此前错杀的题材小票的修复机会。 | 连板数 | 晋级率 | 2025-1-30 | | --- | --- | --- | | 4讲5 | 1/2=50% | 湖南黄金(并购重组+有色金属) | | 2讲3 | 1/14=7% | 百川股份 (化工+半导体) | | 1讲2 | 5/64=8% | 万丰股份（化工） | | | | 皇台酒业(食品饮料) | | | | 天地在线 (AI应用) | | 其他涨停 | | 天奇股份5天3板 (机器人) | 主线热点 近期海外科技巨头再度掀起算力基础设施军备竞赛，亚马逊正与OpenAI洽谈500亿美元投资，微软与Met ...

全文共1098字，阅读大约需要2分钟 不同的合作名目，相似的曝光渴望。 北京商报记者丨张绪旺 "撒钱"的互联网流量方法论曾一度沉寂，但从2025年即时零售大战开始，似乎大厂小厂都找到了新配 方。尤其为迎接2026年春节，春晚营销、春节红包，热闹又回来了。 大手笔接连出现。腾讯元宝发10亿元红包，百度文心5亿元跟进，还决定赞助北京台春晚。总台春晚竞 争异常激烈，字节旗下火山引擎早早拿下独家AI云合作伙伴，同门大模型豆包一直跃跃欲试。 更夸张的是，已有宇树科技、银河通用、魔法原子、松延动力4家机器人公司，均宣布拿下总台春晚合 作权益：机器人合作伙伴、指定具身大模型机器人、智能机器人战略合作伙伴、人形机器人合作伙 伴……不同的合作名目，相似的曝光渴望。 AI企业们的疯狂追逐，并不难理解。每一轮技术浪潮的兴起，都会选择在全民关注的窗口，完成一次 规模宏大的普惠教育与社会认知，同时完成向竞争对手的"饱和攻击"。 AI今年要接班，要站在C位。仔细想想，宇树机器人正是在总台春晚一舞而红，甚至大模型顶流 DeepSeek的出圈节点同样卡在了2025年春节——即便它们当时可能不在营销策略的讨论范围。 从"形单影只"变成群AI竞 ...

Summary of Conference Call Records Industry Overview - The focus is on the AI application sector, which is showing resilience amid a recovering market sentiment. Continuous industrial catalysts are driving the current AI application trend [1][1]. Key Points and Arguments - **Market Sentiment**: There is a stabilization and recovery in market sentiment, indicating a positive outlook for AI applications [1][1]. - **Catalysts for Growth**: - Recent entries into the AI space by companies like Yuanbao and Baidu Wenxin are intensifying competition for AI entry points [1][4]. - Tencent's Yuanbao is testing an AI social feature called "Yuanbao Pai," while Doubao plans to launch a new feature before the 2026 Spring Festival [1][1]. Core Logic for 2026 AI Applications - **Expansion of AI Use Cases**: By 2026, the scenarios for AI applications are expected to diversify and expand significantly [2][2]. - The release of ClaudeSkills and Clawdbot, along with the ClaudeinExcel plugin, enhances the capabilities of large models, extending AI programming into optimizing daily workflows [2][2]. - On January 26, Qwen3-Max-Thinking, a flagship reasoning model, was launched by Qianwen [2][2]. - The anime and drama market is rapidly expanding, with 60% of content creators utilizing AI technology [2][2]. - Significant investments are being made, making commercialization urgent for large model companies, with ChatGPT's first batch of advertisements expected to launch in early February [2][2]. Investment Recommendations - **Focus Areas**: Three main lines of investment are recommended: 1. **Leading Large Model Companies**: These companies have the potential to create super entry points and significant commercialization potential, including Tencent, Alibaba, ByteDance, and MiniMax [2][2]. 2. **Service Providers**: Companies involved in advertising, marketing, e-commerce operations, and e-commerce SaaS services are also highlighted as key areas for investment [2][2]. Additional Important Content - **Upcoming Events**: - Major AI application activities by BAT companies during the Spring Festival are noted [3][4]. - Google’s earnings call is scheduled for February 4 [3][4]. - **Commercialization Trends**: There is a focus on the commercialization of large model companies both domestically and internationally, indicating a competitive landscape [3][4]. - **New Model Development**: The introduction of the DeepSeek model is mentioned, suggesting ongoing innovation in the AI sector [3][4]. - **High Moat Independent AI Products**: Companies like Bilibili, Yidian, Zhidema, and others are recognized for their strong competitive advantages in independent AI applications [5][5].

文｜唐辰 图｜春节红包大战 网络图 红包能撑开多大的AI入口？ 2026年AI圈的年味，在腊八节前后被腾讯和百度拉高。1月25日，腾讯元宝和百度文心先后宣布，要在这个春节分别发出10亿、5亿现金红包，单个红包 最高可得10000元。 双方的玩法有着明显差异：腾讯以优势的"社交裂变"为特色，用户除了可以自己领取红包，还可以分享红包到微信、QQ好友和社群。同时，元宝还在灰 度测试社交类产品元宝派，腾讯董事会主席马化腾称，这本来是绝密的项目。 腾讯的最大依仗还是微信这座社交金矿，其试图用最短的路径，把流量从社交场景导流到AI应用上。 这背后是，腾讯对字节豆包、阿里千问先后在AI2C上跑马圈地，并拿下亿级活跃用户的紧迫感： 10亿红包，本质上是腾讯为争夺用户心智支付的战略性 学费。 图注：元宝红包活动 唐辰截图 百度则联动北京广播电视台春晚，并推出三种核心玩法：看视频领钱、前1000名集齐稀有卡得万元、通过文心助手猜口令。其特征就是和"AI深度绑定"， 提升投入产出比。 这种"撒钱抢人"的戏码，在移动互联网时代并不陌生，它们的意图也很明显，希望在春节黄金窗口期实现用户规模与心智的跨越式增长。 但让人唏嘘的是，AI已经 ...

红包能撑开多大的AI入口？ 2026年AI圈的年味，在腊八节前后被腾讯和百度拉高。1月25日，腾讯元宝和百度文心先后宣布，要在这个春节分别发出10亿、5亿现金红包，单个红包 最高可得10000元。 双方的玩法有着明显差异：腾讯以优势的"社交裂变"为特色，用户除了可以自己领取红包，还可以分享红包到微信、QQ好友和社群。同时，元宝还在灰 度测试社交类产品元宝派，腾讯董事会主席马化腾称，这本来是绝密的项目。 图注：元宝红包活动 唐辰截图 百度则联动北京广播电视台春晚，并推出三种核心玩法：看视频领钱、前1000名集齐稀有卡得万元、通过文心助手猜口令。其特征就是和"AI深度绑定"， 提升投入产出比。 这种"撒钱抢人"的戏码，在移动互联网时代并不陌生，它们的意图也很明显，希望在春节黄金窗口期实现用户规模与心智的跨越式增长。 但让人唏嘘的是，AI已经进入Agent时代，新一轮超级AI入口的争夺，红包还是那个熟悉的"老演员"，成为各家圈定用户的最佳杠杆。尽管打法各异，所 有参与者的核心目标却高度一致。 腾讯的紧迫感 腾讯是春节红包玩法的"鼻祖"。微信支付在2015年凭借春晚"摇一摇"和5亿红包，在移动支付上撕开一道口子，并改 ...

Core Insights - The core viewpoint of the articles is that the competition in the domestic AI application market has solidified into a "three-way battle" among Baidu's Wenxin, ByteDance's Doubao, and Alibaba's Qianwen, each serving as a "super entrance" for consumer AI applications [1][2][3] Group 1: Market Positioning - Baidu's Wenxin has surpassed 200 million monthly active users (MAU), while ByteDance's Doubao reached 172 million MAU and Alibaba's Qianwen exceeded 100 million MAU, indicating a strong competitive landscape [1] - The shift in focus from technical metrics of AI models to their comprehensive application capabilities is expected to dominate discussions from 2025 onwards, with a growing emphasis on practical usability [1][2] Group 2: Competitive Advantages - Doubao benefits from the vast traffic and content ecosystem of ByteDance's platforms like Douyin and Toutiao, allowing it to quickly accumulate a large user base [2] - Qianwen leverages Alibaba Cloud's technological foundation and is closely integrated with Alibaba's e-commerce services, aiming for a seamless transition from conversation to transaction [2][3] - Wenxin's strength lies in its rich application ecosystem, integrating with Baidu's core services such as search, cloud storage, and maps, enhancing its search capabilities in the AI era [3][5] Group 3: Ecosystem Development - Wenxin emphasizes an open ecosystem by allowing third-party applications to connect through its Model Context Protocol (MCP), enabling it to offer a wide range of services from various sectors [3][6] - The integration of services through AI assistants is seen as a way to enhance user experience, providing detailed information and options that traditional apps may not offer [5][6] Group 4: Future Outlook - The competition among the three AI super entrances will depend on advancements in foundational models, tighter integration with existing super apps, and the development of a robust third-party application ecosystem [7] - The long-term vision for AI applications is to act as an "operating system" that seamlessly coordinates information and services, although current capabilities are still in early stages [6][7]

Core Insights - The article discusses the challenges and advancements in the development of Agentic models, emphasizing that the main bottleneck is not the models themselves but the replication of real-world environments and stable access to external interfaces and databases [2][4][5] - It highlights the current limitations of general-purpose models in achieving industrial-level performance across various vertical agent scenarios, suggesting that tailored models for specific applications are more effective [5][12] - The article also explores the evolution of multi-modal models, indicating that while there have been significant advancements, a unified modeling approach for understanding and generating across modalities remains a key goal for the future [17][20] Group 1: Agentic Models - The primary focus is on enhancing models to perform effectively in various vertical agent scenarios, particularly in coding applications [4] - Current general-purpose models lack the capability to achieve stable generalization across diverse environments, necessitating the customization of models for specific applications [5] - The complexity of real-world environments, including external dependencies and interfaces, poses significant challenges for training agentic models [5][6] Group 2: Multi-Modal Models - The transition from single-modal to multi-modal models has introduced visual capabilities into language models, with a focus on aligning text and visual tokens [17][18] - Despite advancements, the industry faces challenges in scaling multi-modal models due to the difficulty in obtaining high-quality, aligned data [18] - Future directions include the pursuit of unified modeling that integrates generation and understanding capabilities, although current results indicate that separate optimization yields better performance [20][21][22] Group 3: Reinforcement Learning and Training Efficiency - The article emphasizes the importance of reinforcement learning systems for continuous model iteration in specific scenarios, with a focus on high efficiency and throughput [6][9] - The scaling of reinforcement learning has not yet reached a consensus in the industry, but there is recognition of its potential to enhance model capabilities significantly [10][11] - Efficient training processes, particularly in generating diverse paths for evaluation, are critical for the success of reinforcement learning in agentic models [9] Group 4: Future Trends and Directions - The article predicts that the development of agentic models with stable and accurate tool-calling capabilities will expand beyond coding applications to a broader range of real-world APIs [28] - The concept of "world models" is discussed, highlighting the evolution from language models to dynamic models that understand physical world operations [26] - The integration of tools into agent development is seen as a crucial pathway for enhancing model capabilities, reflecting the importance of tool usage in human intelligence evolution [25]