证券日报网讯米奥会展11月20日在互动平台回答投资者提问时表示，关于下一代外贸智能体的开发，公 司目前尚未集成阿里巴巴的千问大模型。公司技术路线保持开放和务实，正在积极评估和整合业界多种 大模型技术，旨在根据外贸业务场景的具体需求，灵活选择最优的技术组合来提升智能体的性能与适应 性。在合作层面，公司与阿里巴巴等行业伙伴保持着正常的业务交流与探索。 （文章来源：证券日报） ...

每经AI快讯，有投资者在投资者互动平台提问：请问公司的下一代外贸智能体应用了阿里巴巴的千问 大模型吗？公司和阿里巴巴在ai智能体方面有哪些合作？ 米奥会展（300795.SZ）11月20日在投资者互动平台表示，关于下一代外贸智能体的开发，公司目前尚 未集成阿里巴巴的千问大模型。公司技术路线保持开放和务实，正在积极评估和整合业界多种大模型技 术，旨在根据外贸业务场景的具体需求，灵活选择最优的技术组合来提升智能体的性能与适应性。在合 作层面，公司与阿里巴巴等行业伙伴保持着正常的业务交流与探索。 投资决策需充分考虑市场风险， 建议投资者理性判断、谨慎决策。 （记者 王瀚黎） ...

Summary of Conference Call Records Industry Overview - The conference call focuses on the AI model landscape in China, highlighting the challenges and advancements in the domestic AI industry compared to international counterparts [1][2][4][5]. Key Points and Arguments 1. **Architecture and Innovation** - Domestic AI models heavily rely on overseas architectures like Transformer and MoE, leading to difficulties in surpassing foreign models [1][2]. - There is a lack of self-developed, breakthrough architectural innovations in China, which hampers competitiveness [2]. 2. **Computational Power** - Chinese AI companies have significantly lower GPU computational power compared to international giants like Microsoft, Google, and Meta, often by an order of magnitude [2]. - The ongoing US-China trade war has restricted resource availability, further impacting computational capabilities [1][2]. 3. **Cost and Performance Focus** - Domestic models prioritize inference cost and cost-effectiveness, aligning with local consumer habits, while international models like GPT focus on top-tier performance [1][2]. - The commercial model differences create a substantial gap in model capabilities [2]. 4. **Data Acquisition** - The relatively lenient data laws in China provide an advantage in data acquisition for training models, unlike the stringent regulations in Europe and the US [3]. 5. **Open Source Strategies** - Alibaba adopts a nearly fully open-source strategy, including model weights, code, and training data, to enhance influence and integrate its cloud services [4]. - Other companies like ByteDance and Kuaishou are more selective in their open-source approaches due to their reliance on proprietary technology [4]. 6. **Multimodal Model Developments** - Domestic companies are making strides in multimodal models, focusing on applications in e-commerce and short videos, which cater to local needs [5][6][7]. - Companies like Alibaba, Kuaishou, Tencent, and ByteDance are developing models that integrate text, image, audio, and video generation [7][8]. 7. **MoE Architecture Adoption** - The MoE architecture is becoming standard among major companies, allowing for reduced computational costs and inference times [10]. - Future optimization directions include precise input allocation, differentiated expert system structures, and improved training stability [10][11]. 8. **Economic Viability of Large Models** - Starting mid-2024, pricing for APIs and consumer services is expected to decrease due to the release of previously constrained GPU resources [13]. - The overall cost conversion rate in the large model industry is increasing, despite initial low profit margins [13][14]. 9. **Competitive Differentiation** - Key competitive differences among leading domestic firms will emerge from their unique strategies in technology iteration, data accumulation, and business models [15]. 10. **Future Trends and Innovations** - The focus will shift towards agent systems that integrate user understanding and tool invocation, enhancing overall efficiency [16]. - The MCP concept will gain traction, addressing data input-output connections and reducing integration costs [22]. Additional Important Insights - The acceptance of paid services among domestic users is low, with conversion rates around 3% to 5%, indicating a need for improved user experience to enhance willingness to pay [20][21]. - Successful AI product cases include interactive systems that combine companionship with professional analysis, indicating a potential path for monetization [22]. This summary encapsulates the critical insights from the conference call, providing a comprehensive overview of the current state and future directions of the AI industry in China.

打胜仗的代价相当大，但阿里得到价值重估的机会。 作者 | 彭倩 即时零售大战首个季度战报均已发布，阿里是最大赢家。 整体而言，阿里 Q2营收利润均超出市场预期。剔除掉高鑫零售、银泰出售等因素影响，阿里集团营收同比增长10%，市场则预期同比增长6%；因为大 文娱、盒马等其他业务的经营改善，阿里集团经营利润达到350亿元，同比只下滑了3%；非标下的净利润虽然下滑了18%，但远好于京东的-49%和美 团的-89%，没有惊吓，对市场而言已经是超预期。 实行了2年的"用户为先，AI 驱动"战略成效显著。阿里已经整整3年没有公布用户数据，淘宝闪购的出色表现让阿里有底气重新亮出相关成绩：据蒋凡的 说法，淘宝闪购整体的月度交易用户达到了3亿，对比4月之前增长了200%。淘宝闪购也带动淘宝整体流量提升，拉动淘宝7、8月 DAU 分别同比增长 17%和25%，流量规模显著高于与其他电商平台。 市场更为关注的阿里云业务，Q2首次披露了 AI 收入，占其整体营收比例高达20%，AI 相关产品收入连续八个季度实现三位数同比增长，带动营收同比 增长26%，较上个季度有大幅增长，为3年来最佳。阿里云的 Capex 支出高达386亿元，同比 ...

按照这个节奏，苹果AI系统要来了，因为9月份肯定要发布硬件产品，它不可能在那时，再同步推出一 个新系统。 苹果一贯作风是： 硬件外观迭代时，系统优化和功能升级要提前到位，要么稍后跟进。这样，新设备发布时，软硬结合才 能提供最完整的体验。 这次与百度、阿里的合作听起来很热闹。 表面上看，百度成了主导者，类似于在中国扮演OpenAI的ChatGPT、或谷歌搜索在国际市场中的角色。 而阿里则看似退居幕后。 实际上，从合作份额来看，情况非常有趣。 根媒体公开数据报道：百度、阿里的技术占比费用不同，虽然阿里表面上处在次要地位，实际上，它依 然占大头，凭借阿里现有实力，完全有能力独立承担这个项目。 01 问题是：苹果为什么要采用这种"双核驱动"的AI模式？它背后到底在打什么算盘？可能有几个关键原 因。 一，是数据生态的互补。 阿里基因是什么？电商。它是全球最大的电商交易数据池之一。2025年天猫双十一成交额达到了1.8万 亿，数字背后意味着什么？几乎掌握了用户从浏览、下单到支付的完整消费链路。 百度掌握中文搜索，日均超过60亿次。这些搜索数据涵盖了人们的生活服务需求、知识获取行为，等 等。 所以，一个管"买"，一个管"查 ...

Core Viewpoint - The article discusses the paradox of open-source large models in the AI industry, questioning how these models can generate revenue despite being freely available. It emphasizes that profitability is essential for business operations and suggests various monetization strategies that can be employed by companies in this space [5][8][41]. Group 1: Open Source Models and Revenue Generation - Open-source models have become mainstream, but there is skepticism about their ability to generate revenue [4][7]. - Companies can monetize open-source models through several strategies, such as charging for usage rights of certain models [12][18]. - Successful examples from the open-source world, like Red Hat, illustrate that companies can provide paid solutions around open-source products [9][10]. Group 2: Monetization Strategies - Companies can charge for customized B2B model deployments, which is a significant revenue source [20][33]. - Selling computational power, as demonstrated by DeepSeek, is another viable revenue stream, with reported daily profits of $470,000 and a profit margin of 545% [22][23]. - Open-source products often generate more revenue from services rather than direct product sales, creating an ecosystem that supports monetization [28][30]. Group 3: Market Dynamics and Challenges - The AI industry is still evolving, and many companies are struggling to achieve profitability, with significant investments in GPU resources yielding limited returns [45]. - The article highlights that the current focus for AI companies should be on gaining attention and user engagement rather than immediate profitability [47]. - The competitive landscape necessitates that companies adopt open-source strategies to remain relevant and avoid being overshadowed by leaders like DeepSeek [47][48].