以下文章来源于智能涌现 ，作者邓咏仪 智能涌现 . 直击AI新时代下涌现的产业革命。36氪旗下账号。 2025年，月之暗面从"烧钱换规模"的斗争中暂时脱身，还在海外开发者蹚出一条新路，这是杨植麟在公开信中写下"短期暂不考虑上市"的 底气。 文 ｜ 邓咏仪 编辑 ｜ 苏建勋 来源｜ 智能涌现（ID：AIEmergence） 封面来源 ｜ AI生成 1 2月 末的AI圈异常热闹：智谱、MiniMax的"港股AI第一股"之争刚落幕，2025年最后一天，月之暗面（Kimi）默默甩出最后一 炸：完成5亿美金的新一轮融资。 据了解，本轮融资由IDG领投，阿里、腾讯等 月之暗面老股东超额认购，公司投后估值达43亿美元。据"智能涌现"了解，月之暗 面这一轮超额认购的老股东，还包括高榕创投和今日资本。 超额认购，意味着老股东对被投项目持续看好。 所谓超额认购（Super Pro Rata），通俗来说，这是一种让早期投资者在后续融 资中"加仓"，并扩大持股比例的特殊权利。 举个例子，机构A在投前占月之暗面股比5%，而在引入新股东后，机构A想维持5%的份额，需要继续下注（Pro Rata），那如果 想要获得5%以外的更多份额，就要 ...

1 2月 末的AI圈异常热闹：智谱、MiniMax的"港股AI第一股"之争刚落幕，2025年最后一天，月之暗面 （Kimi）默默甩出最后一炸：完成5亿美金的新一轮融资。 根据月之暗面提供给我们的官方信息，本轮融资由IDG领投，阿里、腾讯等 月之暗面老股东超额认 购，公司投后估值达43亿美元。据《智能涌现》了解，月之暗面这一轮超额认购的老股东，还包括高榕 创投和今日资本。 文｜邓咏仪 编辑｜苏建勋 超额认购，意味着老股东对被投项目持续看好。所谓超额认购（Super Pro Rata），通俗来说，这是一 种让早期投资者在后续融资中"加仓"，并扩大持股比例的特殊权利。 举个例子，机构A在投前占月之暗面股比5%，而在引入新股东后，机构A想维持5%的份额，需要继续 下注（Pro Rata），那如果想要获得5%以外的更多份额，就要加仓更多金额，这就是超额认购(Super Pro Rata)。 除了融资消息以外，12月31日，月之暗面创始人杨植麟也发布内部信，披露了几个关键信号： 加大人才激励：春节前会确定K2 Thinking模型和产品的奖励方案。2026年平均激励会是2025年的 200%，同时大幅上调期权回购额度； ...

Core Insights - The commercialization of large models is facing significant challenges, with many executives leaving key positions in companies referred to as the "six small tigers" of large models, indicating a growing anxiety about monetization strategies [1][2] - Companies are exploring both B2C and B2B paths for commercialization, with a notable shift towards B2B as firms reassess their strategies in response to market pressures [2][3] - The current landscape shows that while some companies report substantial growth in revenue, the majority of over 300 global large model companies have yet to achieve meaningful commercialization [1][2] Company Strategies - MiniMax, Moonlight, and Leap Star focus primarily on B2C products, such as video generation and AI companionship applications, while companies like Zhipu AI and Baichuan Intelligence are more B2B oriented, targeting sectors like retail and healthcare [2][3] - Zhipu AI has reported a projected 100% year-over-year growth in commercialization revenue for 2024, with a significant increase in platform usage [1][2] - The shift from B2C to B2B is evident as companies like Zhipu AI and Zero One Matter adjust their strategies to focus on business clients, moving away from unprofitable consumer offerings [2][3] Market Dynamics - The B2B sector is seeing increased investment in generative AI, with companies prioritizing ROI and efficiency improvements, particularly in areas like software development and marketing automation [3][4] - The profitability of cloud-based services is challenged by product homogeneity and the difficulty in meeting specific client needs, leading to a preference for customized solutions [4][5] - The industry is exploring "deep verticalization," where general large model capabilities are integrated with specialized knowledge in sectors like finance and healthcare to create tailored AI solutions [3][4] Technological Deployment - Most companies in the "six small tigers" utilize cloud-based training and inference, relying on public cloud providers for computational power, with revenue models based on API usage and customized solutions [4][5] - The deployment of AI models on edge devices presents technical challenges due to the high computational and storage demands of large models, necessitating innovations in hardware and model optimization [5][6] - Strategies such as model compression and "edge-cloud collaboration" are being explored to enhance performance while managing resource constraints on end devices [5][6]