基金费率改革或影响短债基金 理财公司考虑三大替代路径 近日，证监会就《公开募集证券投资基金销售费用管理规定（征求意见稿）》公开征求意见，拟针对不 同持有期限设置基金赎回费率下限。业内人士认为，此举或提高短期持有基金的赎回成本，进而影响短 债基金等高流动性产品的投资价值。作为短债基金的重要机构投资者，理财公司正积极寻求应对之策。 记者从业内获悉，理财公司主要考虑三条替代路径：直接进行债券交易、通过专户配置债券、投资债券 ETF及同业存单指数基金。（中国证券报） 月内122只基金开启募集 环比增长45.24% 9月份以来，公募基金发行市场持续活跃。公募排排网数据显示，9月1日至9月16日，全市场共有122只 基金启动募集，相较于8月份同期的84只环比增长45.24%。同时，基金发行市场的"开门红"不仅表现在 数量的增长上，更体现在募集效率的提升上。9月份以来，新基金的平均认购天数较8月份同期的17.42 天缩短了近30%，部分热门产品甚至出现了"一日售罄"的火爆现象。"权益类基金业绩表现突出，赚钱 效应显著，是推动基金发行市场活跃的重要动力。"排排网集团旗下融智投资FOF基金经理李春瑜表 示。（证券日报） 退市公 ...

牛市来了还没上车？上天天基金APP搜索777注册即可领500元券包，优选基金10元起投！限 量发放！先到先得！ "到2035年，人工智能将助力预防超过80%的慢性病；超过90%的中国家庭将拥有智能机器人；人 类将逐渐进入全息生活空间的时代。" 9月16日，华为发布智能世界2035系列报告，展望了未来十年的关键技术趋势以及这些技术对教 育、医疗、金融、制造、电力等行业带来的改变和影响。 华为预测,到2035年 9000 亿 全球Al智能体数量将达到 10 万倍 全社会算力需求提升 500倍 Al存储容量提升 通信联接数量提升 50 % 新能源发电量占比超过 华为常务董事汪涛发表了"探索未知，跃见未来"的主题演讲。汪涛表示："每一次文明的跃迁都源 自人类对未知的不断探索。这份深植于人类基因的探索精神，推动我们不断突破认知与技术的边 100倍 界，走向更加繁荣的智能文明。生成式人工智能正在以我们从未想象过的方式，重新定义未来的可 能性。因此，我们比以往任何时候都更需要前瞻的视野，更需要依靠科技的愿景与假设来指引前 路。" 在华为看来，未来十年， AGI、智能体、自动驾驶、算力 等十大关键技术，将发展成什么样子呢？ ...

温馨提示 ： 点击下方图片，查看运营团队最新原创报告（共235页） 说明： 欢迎约稿、刊例合作、行业交流 ， 行业交流记得先加入 " 机器人头条"知识星球 ，后添加（ 微信号：lietou100w ）微信； 若有侵权、改稿请联系编 辑运营（微信：li_sir_2020）； 正文： 正式官宣， "硅谷系"具身智能机器人创企 [ Dyna Robotics]完成1.2亿美元A轮融资！ 9月16日， 中美两地同时组建团队、 "硅谷系"具身智能机器人创企 【 Dyna Robotics】（ 达纳灵动）官 宣完成 1.2亿美元A轮 融资，本轮融资由 Robostrategy、CRV、firstroundcapital领投，salesforce venture、NVIDIA、Amazon、Samsung next和LG Technology Ventures共同投资。 小编上周（9月8日）刚发文章，透露：" [Dyna Robotics]正进行新一轮融资，预计融资额超1亿美元，目前 正在考虑多 个投资报价，估值达 6 亿美元（含本轮投资） "。 公司上一轮可追溯到 2025年3月26日，完成由硅谷著名VC CRV 和 F ...

"到2035年，人工智能将助力预防超过80%的慢性病；超过90%的中国家庭将拥有智能机器人；人类将逐渐进入全息生活空间的时代。" 9月16日，华为发布智能世界2035系列报告，展望了未来十年的关键技术趋势以及这些技术对教育、医疗、金融、制造、电力等行业带来的改变和影响。 华为常务董事汪涛发表了"探索未知，跃见未来"的主题演讲。汪涛表示："每一次文明的跃迁都源自人类对未知的不断探索。这份深植于人类基因的探索 精神，推动我们不断突破认知与技术的边界，走向更加繁荣的智能文明。生成式人工智能正在以我们从未想象过的方式，重新定义未来的可能性。因此， 我们比以往任何时候都更需要前瞻的视野，更需要依靠科技的愿景与假设来指引前路。" 在华为看来，未来十年，AGI、智能体、自动驾驶、算力等十大关键技术，将发展成什么样子呢？一起来看一下。 趋势一：AGI将是未来十年最具变革性的驱动力量，但仍需克服诸多核心挑战，方能实现AGI奇点突破。因此，走向物理世界是AGI形成的必由之路。 趋势二：随着大模型的发展，AI智能体将从执行工具演进为决策伙伴，驱动产业革命。 趋势三：开发模式迎来变革，人机协同编程成为主流。人类将更专注于顶层设计和创 ...

Core Viewpoint - The article discusses the current state of large language models (LLMs) and the challenges they face in achieving Artificial General Intelligence (AGI), emphasizing the need for new paradigms beyond the existing autoregressive (AR) models [4][10][18]. Group 1: Current Challenges in AI Models - Ilya, a prominent AI researcher, warns that data extraction has reached its limits, hindering the progress towards AGI [2][4]. - The existing LLMs often exhibit significant performance discrepancies, with some capable of outperforming human experts while others struggle with basic tasks [13][15]. - The autoregressive model's limitations include a lack of bidirectional modeling and the inability to correct errors during generation, leading to fundamental misunderstandings in tasks like translation and medical diagnosis [26][27][18]. Group 2: New Directions in AI Research - Elon Musk proposes a "purified data" approach to rewrite human knowledge as a potential pathway to AGI [5]. - Researchers are exploring multimodal approaches, with experts like Fei-Fei Li emphasizing the importance of visual understanding as a cornerstone of intelligence [8]. - A new paradigm, the diffusion model, is being introduced by young scholars, which contrasts with the traditional autoregressive approach by allowing for parallel decoding and iterative correction [12][28]. Group 3: Development of LLaDA-MoE - The LLaDA-MoE model, based on diffusion theory, was announced as a significant advancement in the field, showcasing a new approach to language modeling [12][66]. - LLaDA-MoE has a total parameter count of 7 billion, with 1.4 billion activated parameters, and has been trained on approximately 20 terabytes of data, demonstrating its scalability and stability [66][67]. - The model's performance in benchmark tests indicates that it can compete with existing autoregressive models, suggesting a viable alternative path for future AI development [67][71]. Group 4: Future Prospects and Community Involvement - The development of LLaDA-MoE represents a milestone in the exploration of diffusion models, with plans for further scaling and improvement [72][74]. - The team emphasizes the importance of community collaboration in advancing the diffusion model research, similar to the development of autoregressive models [74][79]. - Ant Group's commitment to investing in AGI research reflects a strategic shift towards exploring innovative and potentially high-risk areas in AI [79].

| ●● AGIX vs 市场大盘 Ticker | 本周表现 | YTD | Return since 2024 | | --- | --- | --- | --- | | AGIX | 3.15% | 25.69% | 69.95% | | S&P 500 | 1.37% | 11.95% | 38.04% | | QQQ | 1.35% | 14.75% | 43.26% | | | 本周表现 | Index Weights | | --- | --- | --- | | Semi & hardware | 0.93% | 23% | | Infrastructure | 2.23% | 45% | | Application | -0.01% | 32% | AGIX 指数诞生于我们对"如何捕获 AGI 时代 beta 和 alphas"这一问题的深度思考。毫无疑问，AGI 代表了未来 20 年最重要的科技范式转换，会像互联网 那样重塑了人类社会的运行方式，我们希望 AGIX 成为衡量这一新科技范式的重要指标，如同 Nasdaq100 之于互联网时代。 「AGIX PM Notes」 是我们对 AGI ...

以下文章来源于创业邦 ，作者薛皓皓 创业邦 . 创业邦，国际创新生态服务平台。我们致力于打造全球化的创业生态，深度服务创新经济及其推动者，并为创业者提供一站式解决方案。 来源 | 创业邦 英伟达已成为当今 AI 时代的基石，而它对初创公司的投资，预示着它对未来十年构建英伟达的大生态的野心。 从 2000 年开始，英伟达就开始进行股权投资。起初，它以收并购为主， 2005 年前后并购了 3Dfx Interactive 、 MediaQ 、 Portalplayer 等公 司。后来，它就按照 风险 投资的方式，进行投资。截 至 目前，它已参与了 200 余项投资，投出了 20 只独角兽。 自 2023 年起，英伟达在一级市场 出手 越发频繁，从 2022 年 20 起左右的投资，上升到 2023 年末大约 50 起。此后的时间，英伟达保持着大 约一年 50~60 起的投资节奏。该时期，英伟达的通用 GPU 成为 AI 的关键基础设施，同时也是英伟达的股价受 AI 催化而翻倍增长的时候。 从投资标的发展阶段而言，英伟达横跨了从种子轮到 D 轮、 E 轮、 F 轮，甚至并购的不同企业发展阶段。 这些投资大多围绕着 ...

Group 1 - OpenAI and Microsoft have released a non-binding cooperation memorandum addressing key issues such as cloud service hosting, intellectual property ownership, and AGI control, but the final cooperation agreement is still pending [1] - OpenAI plans to establish a public benefit corporation (PBC) with a valuation exceeding $100 billion, where a non-profit organization will hold equity and maintain control, becoming one of the most resource-rich charitable organizations globally [1] - OpenAI faces significant cost pressures, expecting to burn through $115 billion before 2029, with $100 billion needed for server leasing in 2030, leaving little room for error in the coming years [1] Group 2 - Utopai, the world's first AI-native film studio founded by a former Google X team, has generated $110 million in revenue from two film projects and secured a spot at the Cannes Film Festival [2] - Utopai has overcome three major challenges in AI video generation: consistency, controllability, and narrative continuity, achieving millisecond-level lip-sync precision with 3D data training [2] - The company positions itself as a content + AI provider rather than a pure tool supplier, receiving support from top Hollywood resources, including an Oscar-nominated screenwriter for the film "Cortes" [2] Group 3 - MiniMax has launched its new music generation model, Music 1.5, capable of creating complete songs up to 4 minutes long, featuring strong control, natural-sounding vocals, rich arrangements, and clear song structure [3] - The model supports customizable music features across "16 styles × 11 emotions × 10 scenes," enabling the generation of different vocal tones and the inclusion of Chinese traditional instruments [3] - MiniMax's multi-modal self-developed capabilities are now available to global developers via API, applicable in various scenarios such as professional music creation, film and game scoring, and brand-specific audio content [3] Group 4 - Meituan's first AI Agent product, "Xiao Mei," has entered public testing, allowing users to order coffee, find restaurants, and plan breakfast menus through natural language commands, significantly simplifying the ordering process [4] - "Xiao Mei" is based on Meituan's self-developed Longcat model (with 560 billion total parameters), capable of fully automating the selection to payment process based on user preferences and location [4] - Despite the advancements, the AI Agent currently has limitations, such as handling complex ambiguous requests and lacking voice response capabilities, with plans for future optimization in personalization and proactive service [4] Group 5 - Xiaohongshu's audio technology team has released the next-generation dialogue synthesis model, FireRedTTS-2, addressing issues like poor flexibility, frequent pronunciation errors, unstable speaker switching, and unnatural prosody [5][6] - The model has been trained on millions of hours of voice data, supporting sentence-by-sentence generation and multi-speaker tone switching, capable of mimicking voice tones and speaking habits from a single audio sample [6] - FireRedTTS-2 has achieved industry-leading levels in both subjective and objective evaluations, supporting multiple languages including Chinese, English, and Japanese, and serves as an industrial-grade solution for AI podcasting and dialogue synthesis applications [6] Group 6 - Bilibili has open-sourced its new zero-shot voice synthesis model, IndexTTS2, addressing industry pain points by achieving millisecond-level precise duration control for AI dubbing [7] - The model employs a "universal and compatible autoregressive architecture for voice duration control," achieving a duration error rate of 0.02%, and utilizes a two-stage training strategy to decouple emotion and speaker identity [7] - The system consists of three core modules: T2S (text to semantics), S2M (semantics to mel-spectrogram), and BigVGANv2 vocoder, allowing for emotional control in a straightforward manner, with significant implications for cross-language industry applications [7] Group 7 - Meta AI has released the MobileLLM-R1 series of small parameter-efficient models, including sizes of 140M, 360M, and 950M, optimized for mathematics, programming, and scientific questions [8] - The largest 950M model was pre-trained using approximately 2 trillion high-quality tokens (with a total training volume of less than 5 trillion), achieving performance comparable to or better than the Qwen3 0.6B model trained on 36 trillion tokens [8] - The model outperforms Olmo 1.24B by five times and SmolLM2 1.7B by two times on the MATH benchmark, demonstrating high token efficiency and cost-effectiveness, setting a new benchmark among fully open-source models [8] Group 8 - An AI agent named "Gauss" completed a mathematical challenge that took Terence Tao's team 18 months to solve, formalizing the strong prime number theorem (PNT) in Lean in just three weeks [9] - Developed by a company founded by Christian Szegedy, an author of the ICML'25 time verification award, Gauss generated approximately 25,000 lines of Lean code, including thousands of theorems and definitions [9] - Gauss can assist top mathematicians in formal verification, breaking through core challenges in complex analysis, with plans to increase the total amount of formalized code by 100 to 1,000 times in the next 12 months [9] Group 9 - Sequoia Capital USA has interpreted the new AI landscape following the release of GPT-5 by OpenAI, which allows for a more natural interaction resembling conversations with a PhD-level expert, incorporating "thinking" capabilities and a unified model to reduce hallucinations [10][11] - Other players have also launched strategic new products ahead of the release, including Anthropic's Claude Opus 4.1 targeting high-risk enterprise scenarios and Google's Gemini 2.5 Deep Think and Genie 3 enhancing reasoning and simulation capabilities [10][11] - The new AI landscape has been reshaped, with OpenAI dominating both open and closed AI ecosystems, Anthropic focusing on enterprise-level precision and stability, and Google emphasizing long-term foundational research [11] Group 10 - DeepMind's science lead, Pushmeet Kohli, revealed that the team targets three types of problems: transformative challenges, those recognized as unsolvable in 5-10 years, and those that DeepMind is confident it can quickly tackle [12] - The team has successfully transferred capabilities from specialized models like AlphaProof to the Gemini general model, achieving International Mathematical Olympiad gold medal levels with DeepThink [12] - The future goal is to create a "scientific API" that allows global scientists to share AI capabilities, lowering research barriers and enabling ordinary individuals to contribute to Nobel-level achievements [12]

Core Insights - The 2025 Bund Conference highlighted the importance of AI in transforming organizational structures and talent development, emphasizing the need for human roles in collaboration with AI [3][5][11] - Key discussions revolved around the shift from traditional job roles to a new paradigm where humans work alongside AI, focusing on creativity, emotional intelligence, and problem definition rather than mere execution [5][7][11] Group 1: Organizational Transformation - Ant Group's Chief Talent Officer, Wu Minzhi, discussed how AGI is driving organizations towards more agile, flexible, and collaborative structures, promoting a virtual project-based approach that enhances team autonomy [5] - The cultural aspect of organizations is crucial, with a focus on creating a safe environment that encourages exploration and embraces uncertainty, highlighting the importance of trust and transparency [5][11] Group 2: Human-AI Collaboration - The concept of "human-machine collaboration" is seen as a new engine for industrial transformation, with companies like BlueFocus integrating AI deeply into performance evaluation and promotion mechanisms, raising AI assessment weight to over 50% [9] - Historical perspectives on AI's role suggest that it acts as an enabler rather than a disruptor, with individuals needing to master AI capabilities and focus on tasks that AI cannot perform, such as emotional and communication skills [7] Group 3: Future of Work - The forum concluded with a consensus on the enduring importance of trust between organizations and employees, even as workflows and efficiency are reshaped by AI [11] - The emergence of "one-person unicorns" reflects a shift towards efficiency over scale, indicating that smaller units can harness significant energy in the AI era [11]

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