（原标题：任泽平回应但斌致歉！） 【导读】"多空之争"终落幕，任泽平回应但斌致歉 中国基金报记者 孙越 1月19日，知名经济学家任泽平在社交媒体上公开回应东方港湾董事长但斌的致歉，为这场持续近两年 的市场"多空"之争画上了句号。 两人的争论始于2024年"9·24"行情启动时期，当时A股市场经历剧烈波动，但斌与任泽平因对后市判断 截然不同而隔空交锋，从观点辩论演变为公开互怼，一度引发市场高度关注。 公开资料显示，东方港湾创立于2004年，系中国首批获得私募基金登记的33家机构之一，公司董事长但 斌于1992年开始投资生涯。 日前，在第三十届中国资本市场论坛上，但斌公开就2024年"9·24"行情的判断向任泽平致 歉。"从'9·24'到现在肯定是个牛市。"但斌认为，当前应着力提升上市公司质量，提高企业的"护城 河"，让其商业模式更加成熟，更能迎接挑战。 任泽平回应：善意收到了，和而不同 1月19日，任泽平在社交媒体上发文，正式回应但斌的公开致歉。 任泽平在文中表示："最近，但斌先生向我致歉，大致意思是2024年9月以来确实是一轮牛市。实事求 是，但斌先生作为业内顶级投资人，水平很高，他的很多观点我看到后，很受启 ...

Core Insights - The article emphasizes that 2026 is poised to be a year of explosive growth in AI applications, driven by recent significant events such as the acquisition of Manus by Meta for several billion dollars, which serves as a catalyst for Chinese entrepreneurs to consider collaboration and international expansion [4][10][13]. Group 1: Trends in AI Entrepreneurship - Trend 1: Overseas markets are becoming the primary birthplace for successful AI applications, with notable examples like Manus achieving an ARR of over $100 million shortly after its launch [10][12][14]. - Trend 2: Collaboration among AI startups is emerging as a vital strategy for survival, contrasting with the competitive acquisition landscape in Silicon Valley. Domestic companies are focusing on partnerships rather than outright acquisitions [16][19]. - Trend 3: Growth strategies are increasingly important, with investors expecting startups to demonstrate rapid scaling and market traction within short timeframes [20][22]. Group 2: Shifts in Business Metrics - Trend 4: The significance of ARR (Annual Recurring Revenue) is diminishing, as the focus shifts to the ability of AI applications to convert token consumption into actual revenue, highlighting the importance of profitability and user retention [25][27][31]. - Trend 5: Traditional industries are undergoing transformation through "AI-native" solutions, with startups leveraging AI to address long-standing inefficiencies and create new business models [32][39][41].

Core Viewpoint - The risk of missing an era is far greater than the risk of prematurely worrying about a bubble, as stated by Dan Bin, emphasizing the importance of adapting to rapid changes in the investment landscape [6][12]. Investment Philosophy - Dan Bin believes that the current AI revolution represents a significant opportunity comparable to the Industrial Revolution, with a potential duration of over ten years [3][16]. - The investment strategy focuses on identifying companies with long-term certainty and strong competitive advantages, particularly in the global technology sector [5][10]. Market Predictions - The year 2026 is anticipated to be a pivotal year for AI application, driven by intense competition among leading tech giants like OpenAI and Google [5][10]. - The current trajectory of the U.S. stock market resembles that of 1998, suggesting a robust recovery and growth in the AI sector [7][10]. AI and Human Development - Dan Bin posits that AI could be a crucial starting point for humanity's transition from carbon-based to silicon-based life, potentially leading to significant advancements in human civilization [4][10]. Investment Opportunities - The focus is on investing in companies that can define the future and possess a wide economic moat, with significant holdings in NVIDIA and Google as examples [5][10]. - There is a strong belief that the AI wave will create structural opportunities in the market, particularly in the context of China's economic landscape [10][11]. Advice for Investors - Ordinary investors are encouraged to leverage their ability to invest heavily in identified opportunities, as this is often more challenging for institutional investors [11][24]. - ETFs are recommended as a practical way for investors to participate in market trends, especially in technology and AI sectors [11][24]. Investment Principles - A fundamental principle is to avoid using leverage for investments, as this is considered a significant risk [12][25]. - The true safety margin in investments lies in the ability of assets to create value over the long term, rather than static valuation metrics [19][20].

Group 1 - The core indicator for evaluating AI startups is user retention, which is essential for determining their future growth potential [1] - Many AI companies that are currently being ridiculed lack user retention, as initial interest does not translate into long-term commitment [1] - The most commercially viable AI applications are often not the most glamorous technologies, but rather those that address practical needs [2] Group 2 - Successful AI commercialization examples include meeting minutes technologies, such as Abridge in the US and Plaud in China, which have achieved significant market traction [2] - The competitive landscape between China and the US in AI shows that most rapidly growing companies in the B2B sector are American, while Chinese companies are primarily focused on B2C applications [2] - Chinese entrepreneurs have opportunities in AI, particularly in enhancing user experience outside of AI, with gaming being a notable area of growth [2] Group 3 - The AI trend for the next 12 months is expected to shift towards applications, following a cycle where hardware and infrastructure have been the focus [3] - The emergence of new applications is anticipated, with predictions that the next major platforms will have already been established this year [3]

Core Viewpoint - The AI industry continues to be a focal point in global technology and capital markets, with evolving development paths and investment directions [1] Group 1: Investment Opportunities - Investment opportunities in the AI sector by 2025 can be centered around three main logics: continuous overseas computing power investment, breakthroughs in domestic large models, and the imminent explosion of AI applications [4] - Major overseas tech companies are significantly increasing their cash flow investments in AI computing power, with Meta's investment cash flow ratio rising from 25%-30% to 92.7% by mid-2025, indicating intense competition for computing resources [4][5] Group 2: Domestic Market Challenges - The development of the domestic computing power chain has been volatile, with capital expenditures from domestic cloud vendors experiencing fluctuations, leading to a lack of significant advancements in large model development compared to overseas counterparts [8] - The primary challenge for domestic computing power lies in the semiconductor sector, where high-end training and inference chips are limited, constraining the willingness and ability of domestic cloud vendors to expand computing power [10] Group 3: Semiconductor and AI Applications - The semiconductor industry's breakthrough, including design, manufacturing, and packaging, is crucial for the rise of the domestic AI ecosystem, with a systemic opportunity expected to emerge by the end of this year or next year [10] - AI applications have yet to produce a truly groundbreaking product, primarily due to a mismatch between technological capabilities, product forms, and user needs [11] Group 4: Key Application Areas - Notable AI application areas include AI smartphones, AR glasses, autonomous driving, and humanoid robots, with the fourth quarter of 2025 expected to be a critical validation point for multiple AI applications [12] - The financial sector shows significant demand for AI talent, as AI technologies can enhance efficiency in areas like smart investment advisory, risk control, and customer service, particularly in a favorable market liquidity environment [12] Group 5: Long-term Outlook - Overall, the three trends in the AI industry are moving positively: continuous growth in overseas computing power investment, acceleration of domestic semiconductor self-sufficiency, and AI applications nearing an explosive phase [13] - Investors are encouraged to focus on different segments based on these trends, such as overseas computing chains and breakthroughs in domestic high-end chips, while closely monitoring product validation in the application sector [13]