Group 1 - McDonald's has increased prices for various menu items by 0.5 to 1 yuan, including a 1 yuan increase for the Big Mac and Double Fillet-O-Fish [2] - Hong Kong's visitor numbers reached approximately 45 million in the first 11 months of the year, surpassing the total for the previous year, with a 12% year-on-year increase [3] - Douyin E-commerce updated its content governance regulations to ensure that creators adhere to scientific evidence and industry standards when publishing content related to counterfeiting and product reviews [4] Group 2 - The National Bureau of Statistics reported a steady expansion in consumer spending, with a focus on enhancing employment and income, and emphasized the need to boost consumer confidence [5][7] - GuoXia Technology's IPO in Hong Kong received over 1800 times oversubscription, raising more than 130 billion HKD, indicating strong interest from global long-term funds [6] - Meituan announced the suspension of its "Tuan Hao Huo" business to focus on exploring new retail formats, reflecting a strategic shift in response to market trends [10] Group 3 - SpaceX is reportedly planning to go public in the second half of next year, with a target valuation of approximately 1.5 trillion USD, which could make Elon Musk the world's first trillionaire [11] - SK Hynix has ordered new thermal compression bonding machines from ASMPT to support the production of HBM4, with a total contract value estimated at around 30 billion KRW [13] - The U.S. chip policy remains inconsistent, as recent approvals for NVIDIA to sell AI chips to China are juxtaposed with legislative efforts to restrict such sales, highlighting ongoing tensions in the semiconductor sector [14]

股票频道更多独家策划、专家专栏，免费查阅>> 责任编辑：栎树 邢自强指，AI需要四大支柱去支撑，中国在AI人才、基础设施及数据方面均具优势。有人才才能改进 演算法，独辟蹊径，节省效率，而全球AI相关的一半人才是中国培养的；基础设施就是算力中心，需 要有大量的电网、发电站及储能设备，连同冷却、基本维护等设施，这些设施在中国均较便宜。要训练 大模型变成最好的AI，便需要大量数据，中国老百姓一直使用的微信、制造业产业链上的第一手资 料，均为训练AI提供大量数据，故此这方面中国亦具有优势。 12月16日，摩根士丹利中国首席经济学家邢自强认为，中美对AI的观点与发展路径截然不同，中国长 远能否超越美国拭目以待，但凭借可靠的人才、基础设施及数据，可弥补GPU的不足。 邢自强表示，美国的AI企业均是超大规模服务商(Hyperscaler)，大家都拼命的砸钱、拼算力，就是要拼 谁能先通过这些军备竞赛，能先达到AGI(通用人工智能)，所以美国走的是一个重量化。中国的AI模型 普遍都属开源性质，一开始想到的是AI本身未必要那么赚钱，而是要让这些AI工具尽快落地市场化， 故中国走的是一个轻量化、比较便宜，但是又可以大量铺开的AI应 ...

邢自强指，AI需要四大支柱去支撑，中国在AI人才、基础设施及数据方面均具优势。有人才才能改进 演算法，独辟蹊径，节省效率，而全球AI相关的一半人才是中国培养的；基础设施就是算力中心，需 要有大量的电网、发电站及储能设备，连同冷却、基本维护等设施，这些设施在中国均较便宜。要训练 大模型变成最好的AI，便需要大量数据，中国老百姓(603883)一直使用的微信、制造业产业链上的第一 手资料，均为训练AI提供大量数据，故此这方面中国亦具有优势。 格隆汇12月16日｜摩根士丹利中国首席经济学家邢自强认为，中美对AI的观点与发展路径截然不同， 中国长远能否超越美国拭目以待，但凭借可靠的人才、基础设施及数据，可弥补GPU的不足。 邢自强表示，美国的AI企业均是超大规模服务商(Hyperscaler)，大家都拼命的砸钱、拼算力，就是要拼 谁能先通过这些军备竞赛，能先达到AGI(通用人工智能)，所以美国走的是一个重量化。中国的AI模型 普遍都属开源性质，一开始想到的是AI本身未必要那么赚钱，而是要让这些AI工具尽快落地市场化， 故中国走的是一个轻量化、比较便宜，但是又可以大量铺开的AI应用模型之路。 ...

OpenAI：人类打字速度将成通用人工智能发展瓶颈 OpenAICodex产品负责人AlexanderEmbiricos表示，人类的打字速度将成为通用人工智能（AGI）的发展 瓶颈，主要原因是人们仍需要通过写提示词（Prompt）来引导AI，并亲自检查、验证AI的输出结果。 智元"擎天租"机器人租赁平台12月22日发布 12月15日，智元机器人宣布，将于12月22日举办全国机器人租赁生态峰会暨"擎天租"平台发布会，推动 机器人租赁产业标准化，规模化发展。 越疆机器人入选"港交所科技100指数" 越疆机器人入选香港交易所近期推出的"港交所科技100指数"。该指数以研发投入、创新能力、行业代 表性为核心筛选标尺，成分股需满足"过去两年研发开支占比不低于3%"等条件。 中国移动与埃斯顿酷卓签署战略合作协议 埃斯顿酷卓12月15日发布消息，近日，中国移动与埃斯顿酷卓正式签署战略合作协议，双方将围绕工业 具身智能、数据价值挖掘、前沿技术创新方面展开战略合作，携手打造"智慧大脑"与"超强神经"，共同 深耕智能制造领域。双方将联合探索5G/5G-A乃至6G网络与具身机器人的深度融合，通过移动通信低延 迟特性，突破机器人远程 ...

Core Insights - The article discusses the transition from the "Scaling Law" to the "Densing Law," emphasizing the need for sustainable development in AI models as data growth slows and computational costs rise [2][3][15]. - The "Densing Law" indicates that model capability density increases exponentially, with capability density doubling approximately every 3.5 months, while the parameter count and inference costs decrease significantly [11][28]. Group 1: Scaling Law and Its Limitations - The "Scaling Law" has faced challenges due to bottlenecks in training data and computational resources, making it unsustainable to continue increasing model size [15][16]. - The current training data is limited to around 20 trillion tokens, which is insufficient for the expanding needs of model scaling [15]. - The computational resource requirement for larger models is becoming prohibitive, as seen with LLaMA 3, which required 16,000 H100 GPUs for a 405 billion parameter model [16]. Group 2: Introduction of Densing Law - The "Densing Law" proposes that as data, computation, and algorithms evolve together, the density of model capabilities grows exponentially, allowing for more efficient models with fewer parameters [11][28]. - For instance, GPT-3 required over 175 billion parameters, while MiniCPM achieved similar capabilities with only 2.4 billion parameters [24]. Group 3: Implications of Densing Law - The implications of the Densing Law suggest that achieving specific AI capabilities will require exponentially fewer parameters over time, with a notable case being Mistral, which achieved its intelligence level with only 35% of the parameters in four months [32][33]. - Inference costs are also expected to decrease exponentially due to advancements in hardware and algorithms, with costs for similar capabilities dropping significantly over time [36][39]. Group 4: Future Directions and Challenges - The future of AI models will focus on enhancing capability density through a "four-dimensional preparation system," which includes efficient architecture, computation, data quality, and learning processes [49][50]. - The article highlights the importance of high-quality training data and stable environments for post-training data, which are critical for the performance of models in complex tasks [68][70]. Group 5: End-User Applications and Market Trends - By 2026, significant advancements in edge intelligence are anticipated, driven by the need for local processing of private data and the development of high-capacity edge chips [11][45][76]. - The article predicts a surge in edge applications, emphasizing the importance of privacy and personalized experiences in AI deployment [76][77].

华尔街多家大行策略师正建议客户在2026年将投资重心从"科技七巨头"转向医疗、工业、能源和金融等传统周期性板块。高盛认 为，这一转变主要基于两点：一是对科技巨头能否持续支撑其高昂估值和AI支出的疑虑加深；二是对2026年美国整体经济前景的乐 观情绪升温。 一份来自华尔街的最新展望报告颠覆了传统认知，指出2026年美国经济面临的最大威胁可能源自金融市场本身。 根据投资研究机构BCA Research的最新展望，2026年投资者面临的核心风险已经发生反转：不再是经济衰退拖垮股市，而是股市 的潜在崩盘可能直接将美国经济推入衰退。这一观点挑战了市场的普遍看法，并指出美国经济的韧性正悬于一个由股市财富支撑的 脆弱平衡之上。 BCA Research在报告中明确指出， 当前美国经济的一个关键支撑来自于约250万"超额退休"人群的消费支出。这部分人群因新冠 疫情后的股市繁荣而提前退休，他们的消费能力与股市表现直接挂钩，形成了一个"对股市敏感"的需求侧。 报告分析，这种结构性变化给美联储带来了棘手的两难。 一方面，这批高技能退休人员的离场加剧了劳动力短缺，使通胀顽固地 维持在3%左右；另一方面，若为抑制通胀而维持高利率，则可 ...

Core Viewpoint - A recent report from BCA Research indicates that the biggest threat to the U.S. economy in 2026 may stem from the financial markets themselves, rather than an economic recession dragging down the stock market. The report suggests that a potential stock market crash could directly push the U.S. economy into recession, challenging conventional market views [1][2]. Economic Structure and Risks - The report highlights a significant structural change in the U.S. labor market, with approximately 2.5 million "excess retirees" whose consumption is closely tied to stock market performance. This group has retired early due to the pandemic and the subsequent stock market boom, creating a demand side that is sensitive to stock market fluctuations [1][3][5]. - The consumption of these retirees injects strong demand into the U.S. economy, but their retirement means they do not contribute to labor supply, leading to a constrained labor market. This situation creates a delicate balance where strong demand exists alongside limited supply, preventing a recession driven by weak demand [5][7]. Federal Reserve's Dilemma - BCA Research outlines a dilemma for the Federal Reserve: maintaining a 2% inflation target while avoiding a recession. The report predicts that the Fed will prioritize preventing a market crash over its inflation target, potentially allowing inflation to rise above 2% and adopting aggressive rate cuts in response to any signs of economic or market weakness [2][8]. Market Concentration and Challenges - The report notes that the current market rally is historically concentrated, with about two-thirds of global stock market value concentrated in U.S. stocks, and 40% of that in just ten stocks. This concentration poses risks, as the fortunes of these stocks are heavily tied to the success of the generative AI narrative [9][11]. - However, there are signs of divergence among leading tech stocks, indicating that the market is not treating all tech stocks as a single entity. This divergence suggests that value investors are still assessing individual company valuations [11][12]. Investment Opportunities - BCA Research suggests that as the era of U.S. tech stocks outperforming the market may be coming to an end, funds could rotate into undervalued sectors and regions, such as healthcare and European markets. Unlike the U.S., Europe does not face inflationary pressures caused by labor market distortions, creating a favorable environment for the bond market [12].

一份来自华尔街的最新展望报告颠覆了传统认知，指出2026年美国经济面临的最大威胁可能源自金融市场本身。 根据投资研究机构BCA Research的最新展望，2026年投资者面临的核心风险已经发生反转：不再是经济衰退拖垮股市，而是股市的潜在崩 盘可能直接将美国经济推入衰退。这一观点挑战了市场的普遍看法，并指出美国经济的韧性正悬于一个由股市财富支撑的脆弱平衡之上。 BCA Research在报告中明确指出，当前美国经济的一个关键支撑来自于约250万"超额退休"人群的消费支出。这部分人群因新冠疫情后的 股市繁荣而提前退休，他们的消费能力与股市表现直接挂钩，形成了一个"对股市敏感"的需求侧。 报告分析，这种结构性变化给美联储带来了棘手的两难。一方面，这批高技能退休人员的离场加剧了劳动力短缺，使通胀顽固地维持在 3%左右；另一方面，若为抑制通胀而维持高利率，则可能刺破股市泡沫，摧毁这部分关键消费，从而引发经济衰退。 因此，BCA Research预测，美联储将把避免市场崩盘置于其2%通胀目标之上，选择容忍更高的通胀率，并可能在任何经济或市场疲软的 迹象出现时采取激进的降息措施。这一政策路径，叠加史上最集中的市场涨势，为 ...

中新网海口12月13日电 (记者张茜翼)2025第十四届上市公司发展年会暨海南自贸港开放机遇交流大会12 日在海口举办。超过400家上市公司、20多家机构赴会，政产学研精英共话封关新局下的政策路径、全 球化机遇。 苏波表示，未来5年工业企业数字化转型是推进智能制造、实现制造业创新发展的主战场，需促进实体 经济与数字经济深度融合，加快数智技术创新，强化算力、算法、数据高效供给，建立健全数据要素基 础制度，以数智技术赋能千行百业。 中国国际经济交流中心副理事长胡晓炼聚焦海南开放特质指出，海南的开放是高水平开放，核心是制度 型开放——通过推进规则、规制、标准、管理与国际高标准经贸规则和良好实践相容衔接，从降低关 税、放宽市场准入等边境开放，转向更注重对接国际通行规则、完善国内制度体系的边境后开放。 大会还邀请国内行业领军企业围绕"AI价值锚点"与"新出海范式"展开智慧风暴，分享一线经验与思考。 (完) （文章来源：中国新闻网） 12月18日，海南自贸港将迎来全岛封关运作。海南国际经济发展局局长唐华在主会场致辞与推介环节表 示，海南自贸港的政策核心是"一线放开、二线管住、岛内自由"的制度型开放，叠加"零关税、低税 率 ...

Core Insights - The next stage of artificial intelligence (AI) development requires overcoming two major challenges: the Transformer architecture and the backpropagation algorithm [1][7][54] - The focus should shift from larger models to creating "living" models that possess native memory, autonomous learning, and continuous evolution capabilities [2][4][48] - This transition signifies a move from centralized cloud computing to decentralized learning, where each device can contribute to knowledge generation [3][5][70] Group 1: Hardware Awakening - The concept of "hardware awakening" suggests that devices can learn and adapt in real-time, transforming them from mere tools into active intelligent agents [4][64] - A multitude of such intelligent agents collaborating in the real world can lead to the emergence of collective intelligence [5][71] - The current reliance on the Transformer model limits the potential for true intelligence, as it does not facilitate autonomous learning or native memory [21][30][76] Group 2: Redefining Value - The future of AI will redefine the value of hardware, moving beyond traditional metrics like memory and processing power to focus on the co-creation of value between users and devices [64][66] - Users should pay for intelligence rather than token consumption, as the latter is seen as an inefficient model [15][19][21] - The emergence of devices with autonomous learning capabilities will enhance user experience and privacy, as data remains localized [68][69] Group 3: Collective Intelligence - Collective intelligence arises when each device possesses its own intelligence and can learn from the physical world, similar to human collaboration [71][76] - True intelligence is characterized by the ability to generate knowledge rather than merely disseminating it, which is a limitation of current large models [75][77] - The path to general artificial intelligence is through collective intelligence rather than the centralized model exemplified by companies like OpenAI [77]