Core Viewpoint - The article discusses the complex relationship between China and the United States, highlighting both competition and cooperation, particularly in the context of economic trade and technology, with a focus on the recent meeting between the leaders of both nations [1][4]. Group 1: U.S.-China Relations - Both countries recognize the intertwined interests between their economies and the necessity to find a path for coexistence, especially with the upcoming U.S. midterm elections in 2026, where economic performance will be a key factor [2][4]. - The current phase of U.S.-China relations is characterized by a temporary easing of trade tensions, with both nations seeking sustainable coexistence amid their deep economic interdependencies [4][6]. Group 2: Investment Outlook - International investors have shown renewed interest in the Chinese market this year, with expectations that the attractiveness of Chinese assets will continue to grow through 2026 [2]. - Market volatility is anticipated in the fourth quarter of 2025, but investors are looking forward to sector rotations, particularly in high-dividend, traditional consumption, and financial sectors, which may enhance overall asset valuations [2][4]. Group 3: AI as a Competitive Arena - The rapid development of generative AI has positioned it as a new battleground between the U.S. and China, with significant implications for global power dynamics [10][12]. - There are differing perspectives on AI's role: while the U.S. sees it as a race for dominance, China views it as a tool for enhancing efficiency across various industries [12][13]. Group 4: Recommendations for Chinese Enterprises - Chinese companies looking to enter the U.S. market should adopt a patient approach, consider joint ventures for cultural understanding and political protection, and prepare for increased regulatory scrutiny [8].

当前，中美两国之间在经济贸易、科技领域都存在竞合关系。积极的信号在于，中美两国领导人于11月 在韩国釜山成功举行会晤，达成很多重要共识，为中美关系这艘巨轮稳健前行校准航向、注入动力，也 向世界传递积极信号。 近期，瑞银全球金融市场部中国主管房东明对话著名国际关系学者、哈佛大学教授格雷厄姆·艾利森 （Graham Allison），就未来的中美关系、人工智能（AI）等问题展开讨论。艾利森是肯尼迪政府学院 的创始院长。他曾担任里根政府国防部长特别顾问、克林顿政府助理国防部长等职。艾利森教授于2012 年提出"修昔底德陷阱"（Thucydides Trap）概念，即一个新崛起的大国必然要挑战现存大国，而现存大 国也必然会回应这种威胁，引发关于美中两国能否跨越"修昔底德陷阱"的思考。 在格雷厄姆看来，中美双方都意识到两个强大经济体之间存在着深度交织的利益关系，必须找到共存之 道。特朗普总统的情况尤为明显——2026年美国中期选举临近，美国经济表现将成为关键因素，而中美 关系正是影响美国经济的核心变量。同时，他也认为，AI 也可能为中美提供少有的合作空间，因为任 何一方都无法单独应对 AI 带来的跨国性风险。因此，尽管 ...

文 | 深毒商业 "（中国AI第一股）这个称号理应属于我们"，智谱CEO张鹏在受访时表示。 这个时候对外宣称自己是"中国AI第一股"，不免会难以服众。 这不禁引人深思，在竞争如此激烈的当下，究竟哪家大模型才配得上"第一"的称号？智谱又有哪些"第 一"呢？ 01 最强大模型， 保质期只有24小时？ 俗话说得好，"文无第一，武无第二"，而大模型之争，就恰如文人论道，难分高下。 北美的四家大模型厂商早就给我们"打了样"：OpenAI的ChatGPT、谷歌的Gemini、Anthropic的Claude以 及xAI的Grok。轮流在各大基准测试中摘取金牌，坐上"SOTA"的宝座。 今天ChatGPT凭借一个多模态创新震撼世界，明天Claude就以超长的上下文或强大的编程能力迎头赶 上；后天Gemini又凭借原生多模态或强大的Agent能力重夺榜首，紧接着Grok又以其独特的实时信息处 理能力和幽默风格吸引眼球。 它们在同一个赛道和评测中轮流第一、轮流SOTA，只不过每一个"最强"的称号都维持不了多久，便被 下一个友商发布的新模型打败，如同手机厂商不断刷新"史上最强"的纪录一样，如此循环往复，形成了 一个永无止境的"军 ...

DeepSeek 一发布模型，总会引起业内的高度关注与广泛讨论，但也不可避免的暴露出一些小 Bug。 比如老外用英文询问，它却在思考过程中切回「神秘的东方文字」。当然，DeepSeek 模型对汉字「情有独钟」的情况早已出现，「极」字 Bug 就是典型 例子。 而这一次，随着新模型 DeepSeek-V3.2 的发布，大家又发现了 DeepSeek 需要优化的地方：其长思考版本（Speciale）暴露出一些 Token 使用效率不佳的问 题。 根据多位研究者反馈，DeepSeek-V3.2 Speciale 在处理复杂任务时出现明显的 Token 消耗异常。具体表现为： 在相同任务上，Gemini 只消耗 2 万 Token，DeepSeek-V3.2 Speciale 却用了 7.7 万，也就是说，它需要 3 倍以上的 Token 才能输出类似质量的结果。 另外，Speciale 版本出现输出内容又长又啰嗦的问题，但最终仍然错的情况，这并不是新问题，而是 GRPO 算法本身的固有缺陷。 实际上，DeepSeek-V3.2 在 Token 消耗方面的异常表现，已经被不少用户与研究者观察到。有社区网友指出，Spe ...

Core Insights - The article discusses the release of the DeepSeek-V3.2 model, highlighting its performance issues, particularly in token consumption and output verbosity, which have raised concerns among users and researchers [1][2][6]. Token Consumption and Efficiency - DeepSeek-V3.2 Speciale exhibits inefficient token usage, consuming 77,000 tokens for tasks where Gemini only requires 20,000, indicating over three times the token expenditure for similar quality results [1][6]. - Users have noted that the generation speed of DeepSeek-V3.2 Speciale is approximately 30 tokens per second, and an increase to around 100 tokens per second could significantly enhance usability and experience [6]. Output Quality and Verbosity - The Speciale version tends to produce lengthy and verbose outputs, often resulting in incorrect responses, which is attributed to inherent flaws in the GRPO algorithm [2][15]. - The model's performance in benchmark tests shows that it has a median score of 76.38, with a median difference of 11.07% compared to other models, indicating a notable gap in efficiency [7]. Comparison with Other Models - In benchmark comparisons, DeepSeek-V3.2 Speciale's token consumption during inference has been reported to be significantly higher than its predecessor, with a consumption of 86 million tokens compared to 62 million for the previous version [7][10]. - The model's performance metrics reveal that it lags behind competitors like Gemini-3.0 Pro in terms of output token delay and efficiency [10][12]. Algorithmic Limitations - The GRPO algorithm, which underpins DeepSeek, has been criticized for introducing biases that lead to longer and often incorrect responses, a problem that persists in the latest model [16][20]. - Length bias, a significant issue in the GRPO algorithm, causes the model to generate longer responses even when they are incorrect, which has been identified as a primary reason for the high token consumption in DeepSeek-V3.2 Speciale [20][23]. Future Directions - The developers acknowledge the need for improved token efficiency as a critical area for future research, aiming to balance performance and cost in subsequent model iterations [14][23].

近日，未可知人工智能研究院院长杜雨博士受邀走进意大利路易斯・圭多・卡利大学（Luiss ROMA）跨文化传播课堂，以 " AI 与叙事的未来：导航 媒体、新闻与战略传播新纪元 " 为主题展开专题分享。 作为意大利知名的综合性大学，Luiss ROMA 以社会科学、商科与传播领域研究见长，其跨文化传播课程聚焦全球视野下的媒介变革与交流创新，此次 邀请杜雨博士分享，旨在为师生搭建中西方 AI 传播实践的对话桥梁。 ▲ 戳蓝 色字关注我们！ 作为人工智能领域的资深专家、中国生成式人工智能数据应用合规指南起草人，杜雨博士的分享围绕 AI 在中国的发展实践、对商业传播的转型影响及 传媒行业的创新应用三大核心展开，为现场师生呈现了一场兼具全球视野与本土洞察的知识盛宴。 中国 AI 发展 从技术浪潮到生态重构，DeepSeek 成破局关键 分享伊始，杜雨博士系统梳理了中国 AI 产业的发展脉络。 他指出，中国 AI 行业已历经 "计算机视觉四小龙" 与 "大语言模型六小虎" 两轮发展热潮，大语言模型的崛起直接推动中国 AI 市场规模持续扩容，目 前全球市场份额已达 20%。 在 "AI+" 国家战略引领下，互联网、电信、金 ...

国泰海通证券认为，近期人形机器人产业在国内外持续取得重要进展，标志着产业正在快速实现商业化 落地，人形机器人行业短期需要重点关注由事件催化的行业景气度波动，而长期则需重点关注产业链上 具备确定性的优质公司。 国海证券认为，电动化与智能化浪潮下，国内外人形机器人产品问世并不断迭代，有望开辟比汽车更广 阔的市场空间，人形机器人产业链将迎来"从0至1"的重要投资机遇。机器人从本体到零部件商持续开展 产品迭代，同时快速推进业务合作和场景应用，积极探索人形机器人规模化量产和商业落地，人形机器 人产业或迎来"ChatGPT时刻"。 上海证券认为，近期国内外产业端迎来密集催化，产业潮起入局者明显增多，国内多数企业纷纷加码具 身智能，海外特斯拉、Figure AI等加速商业化量产步伐。DeepSeek等人工智能公司的涌现推动通用机器 人大模型的发展，助力人形机器人实现具身智能，人形机器人产业链进入"百花齐放，百家争鸣"阶段， 目前人形机器人进入工业场景，已经成为国内外确定性较高的应用趋势，人形机器人商业化落地可期。 近期，人形机器人领域迎来密集利好，头部企业动作频频，产业已开启从技术研发到商业化落地的关键 跨越，A股上市公司 ...

Core Argument - The article discusses Ben Buchanan's "grand bargain" proposal for AI development in the U.S., suggesting a strategic agreement between the tech industry and the government to integrate AI into national defense while ensuring it aligns with democratic values. However, the feasibility of this proposal is questioned due to the contrasting realities of U.S. chip policies and the rapid advancements in AI technology from China [1][5][20]. Group 1: AI Development and Policy Discrepancies - Buchanan's proposal emphasizes the need for a strategic partnership between the tech industry and the government, where the former gains access to energy infrastructure and talent, while the latter integrates AI into national defense [1][20]. - The success of DeepSeek's V3.2 model, which rivals top closed-source models despite U.S. chip export restrictions, challenges the effectiveness of both the "dependency" and "containment" strategies towards China [5][6][20]. - The article highlights a fundamental divide in U.S. AI strategy regarding chip policies towards China, with one faction advocating for strategic dependency and the other for strict containment [2][4][5]. Group 2: Energy Infrastructure Challenges - Buchanan's vision includes a significant increase in energy demand for the AI industry, projecting an additional 50 billion watts by 2028, equivalent to Argentina's total electricity consumption [7][8]. - The U.S. faces a political deadlock in energy policy, hindering the construction of new power plants, which is critical for supporting the growing AI sector [7][8]. - The contrasting ability of China to rapidly mobilize resources for infrastructure development poses a competitive disadvantage for the U.S. [9][10]. Group 3: Talent Acquisition and Immigration Policies - The article notes that 70% of top AI researchers in the U.S. are foreign-born, yet current immigration policies are tightening, which could lead to a significant decline in international student enrollment [10][11]. - There is an inherent conflict between the desire to attract international talent and the increasing national security measures that restrict access to sensitive AI research [11][13]. - The political climate in the U.S. is increasingly hostile towards immigration, complicating efforts to maintain a robust talent pipeline for the AI industry [10][11]. Group 4: Government-Industry Relations - The proposed "grand bargain" faces deep-seated mistrust between the tech industry and the government, with tech companies wary of regulatory overreach and the government skeptical of the industry's commitment to national security [14][15]. - Historical examples of tech companies resisting military collaborations illustrate the challenges in establishing a cooperative relationship [14][15]. - The article argues that achieving consensus on key issues such as AI control and economic benefits distribution is unlikely, complicating the realization of the "grand bargain" [15][19]. Group 5: Long-term Strategic Challenges - The rapid pace of AI development contrasts sharply with the slow-moving U.S. political system, which struggles to implement necessary reforms in a timely manner [16][17]. - The instability of political cycles in the U.S. raises concerns about the sustainability of long-term strategies, as policies can be easily overturned by subsequent administrations [17][20]. - The article concludes that the "grand bargain" is based on overly optimistic assumptions about achieving consensus and cooperation in a fragmented political landscape [20].

Mistral Roars Back With Frontier AI Family That Goes Head to Head With DeepSeek► https://t.co/57kt3KKoOY https://t.co/57kt3KKoOY ...

Core Insights - OpenAI's ChatGPT has transformed global communication and work, with over 800 million users weekly, making it the fastest-growing application in history. OpenAI is now valued at over $500 billion despite significant losses [1][2] - Google has launched its Gemini 3 model, which outperforms OpenAI's GPT-5.1 in various benchmarks, leading to a surge in Google's stock price and market capitalization [2][5] - OpenAI has declared a "Code Red" internally, indicating a heightened state of urgency to improve ChatGPT in response to competitive pressures from Google and other tech companies [5][14] Company Developments - OpenAI's recent restructuring has positioned it as a major player in AI, but it faces intense competition from companies like Google, which has shown significant advancements with its Gemini models [1][2] - The launch of Gemini 3 has led to a notable increase in Google's market value, with its stock rising over 11% in the past month [2] - OpenAI's CEO Sam Altman has communicated a shift in focus towards enhancing ChatGPT's capabilities, postponing other projects like advertising and personal assistant development [5][14] Competitive Landscape - Google’s Gemini 3 has demonstrated superior performance in various AI benchmarks, leading to a 6% decrease in ChatGPT's daily active users since Gemini's launch [11][14] - OpenAI's recent updates to its GPT-5.1 model have been minor, focusing on user experience rather than significant performance improvements, which contrasts with the aggressive advancements made by competitors [10][14] - The competitive landscape is intensifying, with other companies like Anthropic and DeepSeek also releasing new models that challenge OpenAI's dominance [10][14] Financial Considerations - OpenAI is facing substantial financial pressures, with a commitment to invest $1 trillion in AI infrastructure, while currently operating at a significant loss [19][25] - The company has projected revenues exceeding $20 billion this year, but these figures may not cover its extensive capital expenditures [20][25] - OpenAI's reliance on venture capital funding, as opposed to established revenue streams like those of traditional tech giants, raises concerns about its long-term financial sustainability [19][20]