此外，SoIC封装可能在生产过程中遇到了一些问题，但根据定焦数码相机的案例来看，M5 Pro 和M5 Max的制造成本可能会降低，但降幅不大。不过，考虑到目前所有厂商都在应对DRAM危 机，任何成本节约都值得欢迎。SoIC封装或许还能帮助苹果解决M5芯片的一些温度问题，我们 之前报道过，该芯片在高负载运行时温度可高达99摄氏度。 然而，SoIC技术最显著的优势或许在于，它可以让M5 Pro和M5 Max在芯片上拥有独立的CPU和 GPU模块，从而根据用户不同的工作负载实现独特的配置。当然，我们必须对这一传闻持保留态 度，等待更多细节公布，因为苹果随时可能给我们带来意想不到的惊喜。话虽如此，让我们拭目 以待吧。 参参考考链链接接 h tt ps://wc c ft e c h . c om/m5 - p r o - a n d -m5-ma x-l a unc h-r umor e d-t o- ha ppe n-i n-ma r c h - n ew- p a c k a g i n g -t o -r e d u c e - c o sts/ 如果您希望可以时常见面，欢迎标星收藏哦~ 一月即将结束，翘首期盼 ...

2025年底至2026年初，东南亚汽车产业政策迎来调整窗口。 泰国大幅调降新能源汽车税率，马来西亚结束进口纯电动车路税豁免，政策导向从普惠性消费补贴转向精细化的产业引导。与此同时，中国汽车企业加速 推进本地化布局，从生产制造到销售服务，构建全产业链竞争力。 政策调整：从"以市场换销量"到"以规则换产业" 东南亚主要汽车市场正对产业激励政策进行方向性修正，试图以市场换产业，以税收和准入为杠杆，引导国际资本与技术在当地扎根。 泰国新政最具代表性。2026年新版机动车消费税将纯电动汽车税率一举降至2%，但对享受低税的插电混动车型提出附加条件：至少搭载两套ADAS系 统，且电池须为泰国本土制造。 这一政策"组合拳"意图明确：短期通过极低税率迅速扩大电动车市场份额，挤压燃油车空间；长期则以税收优惠为筹码，强制引导电池等核心部件的本土 化生产，系统性构建本土电动车产业链。不过，这种"强制"本土化也像是一场"对赌"，如果本土供应链无法在成本与质量上及时跟进，反而可能延缓产业 整体发展步伐。 马来西亚则展现了精细化治理的政策转向。政府终止了进口纯电动车的路税豁免，所有电动车均需按电机功率缴纳年度路税。五菱缤果等低功率车型每年 ...

Core Insights - DeepSeek, a Chinese AI startup, is set to launch its next-generation AI model V4 in mid-February, which is expected to outperform competitors like Anthropic's Claude and OpenAI's GPT series [1] - The rapid development of AI in China has narrowed the gap with the US, with experts noting that the progress made in just one year is significant [1][2] Group 1: Company Developments - DeepSeek's R1 model was launched last year and completed training in just two months at a fraction of the cost incurred by US companies, achieving comparable performance to ChatGPT and Meta's Llama [2] - Chinese open-source AI models account for nearly 30% of global AI technology usage, with companies like Airbnb and Meta utilizing models developed by Alibaba [3] - Alibaba has released nearly 400 open-source models, with over 18 million derivatives and 700 million downloads, showcasing its significant role in the global AI landscape [3] Group 2: Competitive Landscape - The US AI strategy focuses on high-performance closed-source models and platform products, while China emphasizes open-source models and rapid industrial application [4] - While the US leads in cutting-edge model capabilities, China excels in engineering efficiency and speed of deployment, with no significant time lag in these areas [5] Group 3: Future Trends - The next significant advancements in AI are expected to occur in areas such as humanoid robots integrated with large models, industrial applications, and breakthroughs in low-cost inference and edge computing [10] - The AI toy industry is projected to reach a milestone of 1 million units sold, which will generate substantial interaction data, enhancing model capabilities and establishing AI toys as essential daily items [11]

Summary of Maravai LifeSciences Conference Call Company Overview - **Company**: Maravai LifeSciences (NasdaqGS:MRVI) - **Date of Conference**: November 12, 2025 - **Key Speaker**: CFO Raj Asarpota Key Points Strategic Review and Restructuring - Maravai has undergone a strategic review leading to a restructuring aimed at achieving over **$50 million** in annualized expense savings, with **$3 million** realized in Q3 and an expected **$7 million** in Q4 [9][10][11] - The company has streamlined operations by removing layers built during the COVID years, enhancing decision-making and forecasting capabilities [10][11] Financial Performance and Forecasting - Q3 nucleic acid revenues were down **20%** to **$25 million**, compared to **$30 million** in the first half of the year, but the decline rate is decreasing [24][28] - The company expects strong double-digit growth in Q4, supported by a solid order volume and improved forecasting accuracy [29][31] - For 2024, Maravai anticipates **$10 million to $20 million** in COVID-related revenues, having had **zero** COVID revenue in the current year [80][81] Industry Outlook - The biopharma sector shows signs of recovery, with larger pharma companies active and smaller biotechs rationalizing their programs [39][40] - mRNA therapies are gaining traction beyond COVID, particularly in oncology and autoimmune conditions, which are seen as future growth drivers [40][49] Customer Base and Market Dynamics - Emerging biotech companies are crucial for early discovery and future revenue growth as they transition to larger orders [58] - The company is focusing on larger deals while still maintaining e-commerce initiatives for smaller orders [21][22] Product Development and Innovation - Maravai is optimistic about the potential of new products, particularly in mRNA applications, which are expected to increase dosage and efficiency [106][110] - The combination of Mocktail and CleanCap technologies is anticipated to enhance customer workflows and product durability [109][110] Financial Projections - The company aims for mid to high single-digit growth in the long term, aligning with peers like Thermo and Danaher [116] - Positive EBITDA is expected for the full year, driven by cost reductions and improved product mix [117][120] Customer Concentration - Nakulai has become a significant customer, contributing to revenue in 2024, although the company does not rely heavily on them for forecasts [84][91] - Pfizer remains committed to mRNA therapeutics despite some pipeline adjustments, alleviating concerns about customer concentration [74][80] Regional Performance - Growth in the BST segment was **7%** in the Americas and **17%** in Europe, while growth in China was muted due to tariff-related actions [97][102] - The company expects growth in China in 2026, with a stable outlook for European operations [104][102] Additional Insights - The company is focused on leveraging its scientific credibility and customer relationships to drive future growth [9][40] - There is a strong emphasis on improving operational efficiency and decision-making processes to enhance profitability [121][122]

Summary of Alibaba's Open-Source AI Developments Industry and Company Involved - **Company**: Alibaba Group - **Industry**: Open-source Artificial Intelligence (AI) Core Points and Arguments - **Initial Challenges**: Alibaba faced difficulties in getting its various business units to adopt its Qwen AI models, with some teams preferring to use models from other companies like Meta's Llama until 2024 [7][8][9] - **Current Position**: Alibaba has emerged as a leader in open-source AI globally, surpassing Meta's Llama in several benchmarks, and its Qwen models are now preferred by business users for their broader range and lower operational costs compared to competitors [9][10] - **Customer Adoption**: As of January 2024, over 290,000 customers across various industries, including automotive, healthcare, education, and agriculture, were using Qwen models [10][11] - **Global Expansion**: Alibaba Cloud is actively working to increase the global presence of Qwen models, with international collaborations, such as a Tokyo-based AI developer using Qwen for Japanese language models [10][11] - **Impact on AI Adoption**: The success of Qwen and DeepSeek indicates a shift in the global AI landscape, with Chinese firms starting to lead in open-source AI, which could reshape the global AI software ecosystem [13][14] Additional Important Content - **Internal Dynamics**: Alibaba's decision to allow its business units to operate autonomously has led to increased competition and innovation within the company, ultimately benefiting the development of Qwen models [26][28] - **Leadership Changes**: The appointment of Eddie Wu as CEO in September 2023 marked a renewed focus on AI strategy, with the company emphasizing the importance of Qwen models [38][41] - **Model Development**: The Qwen team has made significant strides, with the release of Qwen3 in April 2024, which includes eight open-source models designed for various tasks [58][59] - **Competitive Landscape**: Despite initial successes, the emergence of DeepSeek's R1 model has created pressure on Alibaba to continuously innovate and improve its offerings [48][50][52] - **Future Collaborations**: There is a growing trend among Alibaba's business units to collaborate on AI projects powered by Qwen3, indicating a shift towards more integrated operations [64] This summary encapsulates the key developments and strategic shifts within Alibaba's open-source AI initiatives, highlighting its competitive positioning and future potential in the global AI landscape.

Core Insights - The AI industry is currently experiencing a significant debate over the effectiveness of pre-training models versus first principles, with notable figures like Ilya from OpenAI suggesting that pre-training has reached its limits [1][2] - The shift from a consensus-driven approach to exploring non-consensus methods is evident, as companies and researchers seek innovative solutions in AI [6][7] Group 1: Industry Trends - The AI landscape is witnessing a transition from a focus on pre-training to exploring alternative methodologies, with companies like Sand.AI and NLP LAB leading the charge in applying multi-modal architectures to language and video models [3][4] - The emergence of new models, such as Dream 7B, demonstrates the potential of applying diffusion models to language tasks, outperforming larger models like DeepSeek V3 [3][4] - The consensus around pre-training is being challenged, with some experts arguing that it is not yet over, as there remains untapped data that could enhance model performance [38][39] Group 2: Company Perspectives - Ant Group's Qwen team, led by Lin Junyang, has faced criticism for being conservative, yet they emphasize that their extensive experimentation has led to valuable insights, ultimately reaffirming the effectiveness of the Transformer architecture [5][15] - The exploration of Mixture of Experts (MoE) models is ongoing, with the team recognizing the potential for scalability while also addressing the challenges of training stability [16][20] - The industry is increasingly focused on optimizing model efficiency and effectiveness, with a particular interest in achieving a balance between model size and performance [19][22] Group 3: Technical Innovations - The integration of different model architectures, such as using diffusion models for language generation, reflects a broader trend of innovation in AI [3][4] - The challenges of training models with long sequences and the need for effective optimization strategies are critical areas of focus for researchers [21][22] - The potential for future breakthroughs lies in leveraging increased computational power to revisit previously unviable techniques, suggesting a cycle of innovation driven by advancements in hardware [40][41]

编 译 / 郑浩钧 设 计 / 师 超 比亚迪的出海战略正在稳步实施。2023年5月开始规划的比亚迪印尼工厂将按计划在今年底竣工， 可为当地新增超18000个就业岗位。除了生产制造外，比亚迪还计划在印尼工厂建设研发中心、培 训设施等等，形成一个更完整的电动汽车生态系统。目前，比亚迪在印尼的电动汽车市场中已经占 据三分之一以上的份额。 1月20日，据路透社报道，比亚迪印度尼西亚公司总裁赵鹰（Eagle Zhao）表示，比亚迪计划在2025 年底前建设完成投资10亿美元的印度尼西亚比亚迪工厂。在长期规划中，该工厂主要面向出口市 场。 "我们在印尼当地建设工厂的每一个步骤都非常顺利，都在按计划进行。我们会信守承诺，到2025 年底，我们将完成工厂的建设工作，"赵鹰在接受路透社和CNBC印尼频道的联合采访时表示。 比亚迪印尼工厂位于西爪哇省梳邦的"梳邦智能城市"（Subang Smartpolitan）工业区内，年产能为 15万辆电动汽车。凭借这项投资，比亚迪运往印度尼西亚的汽车将暂时免征进口税，这一政策旨在 刺激电动汽车需求，同时吸引汽车制造商在印尼投资。印尼政府的目标是到2030年在国内生产60万 辆电动汽车。 ...