Altman称，这两款模型可在个人电脑（PC）甚至手机上本地运行，无需互联网连接，为用户提供完全的控制权和隐私保护。 同时，亚马逊宣布将首次向客户提供OpenAI的模型，计划在其Bedrock和SageMaker平台上提供OpenAI的开放AI权重新模型。这是云计算巨头亚马逊首次提 供OpenAI的产品。 gpt-oss-20b和120b的发布标志着OpenAI战略转向的重要节点，此前该公司多次推迟开放权重模型发布，并进行了广泛的安全测试和评估，以确保模型不被恶 意利用。 所谓的开放权重是介于开源和闭源的一种中间形态，意味着企业和政府机构可以自主运行该模型，因为他们可以查看模型的权重并进行修改。而开源的情况 下，用户可以查看模型的源代码组成，有时包括训练和权重分配方式。 OpenAI向开源模型迈出重要一步：六年来首次推出开放权重模型。 美东时间8月5日周二，OpenAI发布了两个开放权重语言模型gpt-oss-120b和gpt-oss-20b。这是OpenAI自2019年推出GPT-2以来首次发布开放权重模型，也是 OpenAI与微软签署独家云服务协议六年来问世的首批此类模型。 评论认为，OpenAI的新模型 ...

阿里巴巴集团重点展示了"开源"的AI（7月26日，上海市） 中国国内完成备案的AI模型数量半年增加了4成，斯坦福大学的研究报告指出"中国的模型正逐渐赶上美 国"。由于中国的AI多为开源模型，日本AI开发中也在大量采用。美国试图遏制中国AI发展。但专家 称"能否遏制中国崛起仍是未知数"…… 中国的生成式AI（人工智能）正在猛追美国。中国国内完成备案的AI模型数量半年增加了4成，与美国 企业的性能差距也在缩小。7月26日在上海开幕的"世界人工智能大会"的参加企业比去年增加6成，阿里 巴巴集团等展示了最新技术。在美国试图关闭人才交流大门的背景下，中国则寻求在相关领域获得引领 世界的地位。 2018年开始举办的世界人工智能大会今年约有800家企业参加，比2024年增加了约300家。除了40多款 AI模型之外，还展示了60多款机器人，在性能和创新方面展开了竞争。 据路透社报道，中国国务院总理李强在开幕式发表演讲时指出，全球人工智能治理仍然分散。虽然没有 提到美国，但指出人工智能可能成为少数国家和企业的排他性游戏。还提出芯片供应短缺和人才交流受 限等课题，提倡成立AI相关的合作组织。 针对生成式AI，中国提出了在监管的同 ...

Core Insights - The foundational large models are not only the core engine of generative AI but are also shaping the future of computing [2] - There has been a significant increase in model API spending, which rose from $3.5 billion to $8.4 billion, indicating a shift in focus from model training to model inference [2] - The emergence of "code generation" as the first large-scale application of AI marks a pivotal development in the industry [2] Group 1: Market Dynamics - Anthropic has surpassed OpenAI in enterprise usage, with a market share of 32% compared to OpenAI's 25%, which has halved from two years ago [9][12] - The release of Claude Sonnet 3.5 in June 2024 initiated Anthropic's rise, further accelerated by subsequent releases [12] - The code generation application has become a killer app for AI, with Claude capturing 42% of the market, significantly outperforming OpenAI's 21% [13] Group 2: Trends in Model Adoption - The adoption of open-source models in enterprises has slightly declined from 19% to 13%, with Meta's Llama series still leading [17] - Despite the continuous progress in open-source models, they lag behind closed-source models by 9 to 12 months in performance [17][20] - Developers prioritize performance over cost when selecting models, with 66% opting to upgrade within their existing supplier ecosystem [24][27] Group 3: Shift in AI Spending - AI spending is transitioning from model training to inference, with 74% of model developers indicating that most of their tasks are now driven by inference, up from 48% a year ago [31]

导读：2025年大概率是人工智能真正的元年。在这一年的春节前后，DeepSeek推出的V3和R1模型震撼了全球，不仅是因为这个模型仅仅用了7天 就突破了一亿用户数，而且还采用了完全开源的方式，实现了知识的平权。 7月，世界人工智能大会上，不少开源大模型登上了展台，关于AI大模型的"开源与闭源"之争再次成为焦点话题之一，来自全球的科技领袖、企业家 和学者就此展开深入探讨。 其实，在DeepSeek模型推出后没几天，Open AI首席执行官Sam Altman就承认Open AI选择闭源模型，是"站在了历史的错误一边"。那么，为什 么开源模型那么重要，以及为什么是中国在开源模型上发展迅速呢？ 在此前的一次访谈中， 诺安基金研究部总经理邓心怡把模型开源比作"科技民主化"， 原来只能"厨师做菜"，通过模型的开源每一个人都可以根据自 己的喜好"做菜"，并且二次销售出去。邓心怡认为，"科技每一次技术变革都不会是孤岛式的，而是体系化的，通过体系化的科技变革，将启发新的 经济范式革新。" "开源"是每一次科技革命背后的第一推动力，也是诺安科技组的底层价值观。他们在近期的世界人工智能大会上，对各类科技创新产品做了长达5个 小时 ...

Core Insights - The competition in large models has shifted to a contest between Chinese and American AI, with Chinese models potentially setting new open-source standards [3][6][10] - The rapid development of Chinese models like GLM-4.5, Kimi 2, and Qwen 3 indicates a significant shift in the landscape of open-source AI [6][10] - The importance of effective evaluation metrics for models is emphasized, as they can significantly influence the discourse in the AI community [5][24][25] Group 1 - The emergence of Chinese models as potential open-source standards could reshape the global AI landscape, particularly for developing countries [6][10] - The engineering culture in China is well-suited for rapidly implementing validated models, which may lead to a competitive advantage [8][10] - The talent gap between institutions is not as pronounced as perceived; efficiency in resource allocation often determines model quality [5][16] Group 2 - The focus on talent acquisition by companies like Meta may not address the underlying issues of internal talent utilization and recognition [15][18] - The chaotic nature of many AI labs can hinder progress, but some organizations manage to produce significant results despite this [20][22] - The future of AI evaluation metrics will likely shift towards those that can effectively measure model capabilities in real-world applications [23][24] Group 3 - The challenges of reinforcement learning (RL) and model evaluation are highlighted, with a need for better benchmarks to assess model performance [23][26] - The complexity of creating effective evaluation criteria is increasing, as traditional methods may not suffice for advanced models [34][36] - The long-term progress in AI may be limited by the need for better measurement tools and methodologies rather than just intellectual advancements [37][38]

Macro Strategy - Public fiscal expenditure in June showed a year-on-year growth rate of 0.38%, with a cumulative growth rate of 3.4% for the first half of the year, maintaining around 4% overall. However, the fiscal revenue and expenditure gap for the first half of the year was -25,705 billion, higher than the -20,658 billion in the same period of 2024, indicating no improvement in fiscal conditions [2][3] - The LPR remained unchanged in July, with the one-year LPR at 3.00% and the five-year LPR at 3.50%. This stability aligns with market expectations, reflecting the positive effects of the LPR adjustment made in May [2][3] Stock Market Overview - A-share indices showed a fluctuating upward trend from July 21 to July 25, with the Shanghai Composite Index rising by 1.67%, the Shenzhen Component Index by 2.33%, and the ChiNext Index by 2.76%. The STAR Market Index saw the highest weekly fluctuation at 4.36% [3][5] - The market's upward momentum is primarily driven by the commencement of the Yarlung Tsangpo River downstream hydropower project, boosting infrastructure-related sectors, and the continued strength of the technology sector. The GDP growth for the first half of the year was 5.3% year-on-year, laying a foundation for the market's upward trend [5][6] Investment Recommendations - The A-share market is expected to operate in a "slow bull" manner in 2025, supported by policies aimed at stabilizing the stock market and overlapping trends from the new "National Nine Articles" and similar to the "Four Trillion" investment [7] - Key sectors to focus on in 2025 include technology, green energy, consumption, and infrastructure, as highlighted in the government work report [7] - In the short term, the market may experience downward adjustments in August due to uncertainties surrounding US-China tariff negotiations, despite the overall positive economic performance in the first half of the year [7] North Exchange Overview - As of July 25, 2025, the North Exchange had 268 listed stocks, with an average total market value of 8,520.87 billion, an increase of 2.36% from the previous week [9][10] - The liquidity of the North Exchange improved significantly, with an average trading volume of 1.427 billion shares, up 39.13%, and an average trading value of 31.082 billion, up 42.36% [10] Industry Insights Semiconductor Industry - The company Micron is leveraging an AI+SaaS strategy to enhance its platform and integrated development path, significantly improving its competitive advantage. In 2024, the AI+SaaS business revenue reached 842 million, accounting for 54% of total revenue [32][33] - The marketing SaaS market in China is projected to grow from 35.6 billion in 2024 to 74.5 billion by 2027, with a CAGR of 29.3%, indicating substantial growth potential [33] Pharmaceutical Industry - The ADC (Antibody-Drug Conjugate) market is experiencing explosive growth, with the global market size expected to rise from 7.9 billion in 2022 to 14.1 billion in 2024, and projected to exceed 68.5 billion by 2030, reflecting a CAGR of 30.1% [26][27] - The CDMO (Contract Development and Manufacturing Organization) sector is becoming increasingly essential due to the high technical barriers of ADC drugs, with the market size expected to grow from 0.1 billion in 2018 to 2.1 billion in 2022, and projected to reach 2.45 billion by 2030 [27][28] Investment Suggestions - The semiconductor sector is expected to benefit from the growth of KA clients and the rapid deployment of AI applications, leading to a high growth period for the company's SaaS business [36] - In the pharmaceutical sector, companies with ADC-related technology reserves, such as WuXi AppTec and Haoyuan Pharmaceutical, are recommended for investment due to their significant growth potential in the CDMO space [30][31]

Core Insights - The World Artificial Intelligence Conference 2025 in Shanghai showcased global tech giants, with Alibaba prominently featuring its advanced AI models [1][3] - Alibaba launched three AI models in a week, including Qwen3, which is now recognized as the "strongest open-source model globally," alongside its other models, Qwen3-Coder and the latest version of Qwen3 [3][6] - The conference theme emphasized the need for open-source collaboration versus closed-door competition in the AI landscape, with Alibaba leading the charge for open-source initiatives [5][13] Group 1: Model Launches and Performance - Alibaba's recent model releases include Qwen3, Qwen3-Coder, and the latest Qwen3 inference model, all achieving top rankings in their respective categories [3][9] - Qwen3-Coder significantly enhances programming efficiency, allowing novice programmers to accomplish tasks in a fraction of the time typically required [9][12] - The Qwen3 inference model's performance is on par with leading closed-source models like Gemini-2.5 pro and o4-mini, showcasing Alibaba's competitive edge in AI technology [9][11] Group 2: Infrastructure and Investment - Alibaba has established itself as the only vertically integrated full-stack AI company in China, with a robust AI infrastructure that includes data centers and advanced computing capabilities [11][12] - The company has consistently invested in AI and cloud computing, with AI-related revenue showing triple-digit growth over the past seven quarters [12] - Alibaba plans to invest 380 billion RMB (approximately 53 billion USD) over the next three years to enhance its cloud and AI hardware infrastructure, surpassing its total investment in the past decade [15] Group 3: Open Source vs. Closed Source - The newly released models are all open-source, allowing for widespread access and modification, contrasting with the closed-source models from U.S. companies [13][14] - Alibaba's open-source approach is seen as a response to the competitive landscape, aiming to democratize access to advanced AI technologies globally [14][15] - The open-source models from Alibaba are expected to challenge the dominance of U.S. closed-source models, fostering a new wave of innovation in AI [14][15]

Core Viewpoint - The article highlights the launch of Step 3, a new generation of open-source base model by Jieyue Xingchen, which is positioned as a leading open-source VLM (Vision-Language Model) that excels in various benchmarks and has significant commercial potential [1][2][11]. Group 1: Model Features and Performance - Step 3 is recognized for its strong performance, surpassing other open-source models in benchmarks such as MMMU, MathVision, and SimpleVQA [1][41]. - The model integrates multi-modal capabilities, combining text and visual understanding, which is essential for real-world applications [10][39]. - Step 3 is designed to balance intelligence, cost, efficiency, and versatility, addressing key challenges in AI deployment [7][8]. Group 2: Technical Innovations - The underlying architecture of Step 3 utilizes a proprietary MFA (Multi-matrix Factorization Attention) design, optimizing for efficiency and performance, particularly on domestic chips [29][31]. - The model features a total parameter count of 321 billion, with 316 billion dedicated to LLM (Large Language Model) and 5 billion for the visual encoder, showcasing its extensive capabilities [33][34]. - Step 3 employs advanced distributed inference techniques, enhancing resource allocation and reducing operational costs [38]. Group 3: Commercialization and Market Impact - The launch of Step 3 marks a significant step towards commercialization for Jieyue Xingchen, with expectations of substantial revenue growth, projected to approach 1 billion yuan in 2025 [54]. - The model has already been integrated into various smart devices, with partnerships established with over half of the top 10 domestic smartphone manufacturers [54]. - The establishment of the "Model-Chip Ecological Innovation Alliance" with multiple chip manufacturers signifies a strategic move to foster collaboration and reduce costs in the AI ecosystem [51][52]. Group 4: Industry Positioning - Step 3 is positioned as a solution to the pressing industry need for a practical, open-source multi-modal reasoning model, filling a significant market gap [58][60]. - The article emphasizes the shift from competitive pricing strategies to collaborative innovation as a sustainable growth path for the industry [59][60]. - Jieyue Xingchen's rapid iteration and comprehensive model matrix have solidified its reputation as a leader in the multi-modal AI space [57].

Core Insights - The article highlights the rapid advancements in open-source AI models, particularly focusing on the Qwen3 series, which has achieved significant milestones in performance and capabilities [1][2][3]. Group 1: Model Performance - The newly released Qwen3-235B-A22B-Thinking-2507 model has been recognized as the "strongest open-source model globally," surpassing top closed-source models like Gemini-2.5 Pro and o4-mini [3][7]. - In the "final exam for humans," the latest model scored 18.2, an improvement from 11.8 in the previous version, outperforming competitors such as DeepSeek-R1-0528 and OpenAI o4-mini [13][14]. - The Qwen3 series has achieved state-of-the-art (SOTA) results in various benchmarks, including MMLU-Pro, GPQA, and LiveCodeBench, demonstrating superior performance in knowledge, reasoning, and programming tasks [11][16][32]. Group 2: Open-Source Impact - The rapid release of three models in a short period has positioned Qwen3 as a leader in the open-source AI landscape, with significant interest and usage reflected in API call volumes exceeding 100 billion tokens [6][31]. - The article emphasizes that the advancements in open-source AI, particularly from Chinese companies like Alibaba, are reshaping the global landscape, with Qwen models surpassing previous leaders like the Llama series [33][37]. - Alibaba plans to invest over 380 billion yuan in cloud and AI hardware infrastructure over the next three years, indicating a strong commitment to enhancing its AI capabilities [38]. Group 3: Industry Recognition - The achievements of the Qwen3 series have garnered attention from industry leaders, with discussions highlighting the success of open-source models and their potential to challenge established closed-source counterparts [29][36]. - The article notes that the speed of development in China's open-source AI sector is rapidly closing the gap with closed-source models, suggesting a shift in the competitive landscape [39][40].

Group 1 - The core focus of the article revolves around the recent developments in the American AI sector, particularly the restructuring of Meta's AI team and the competitive landscape with Chinese open-source models [1][2][3] - Meta's AI team has undergone significant changes, with a large number of new hires and the departure of older staff, indicating a shift in strategy to improve performance in AI model development [2][3][4] - The article highlights the increasing prominence of Chinese teams in the open-source AI model space, suggesting that Meta's Llama series has fallen behind compared to its Chinese counterparts [2][3][4] Group 2 - The restructuring at Meta is seen as a necessary move to maintain competitiveness, especially as the company has ample resources but has not delivered satisfactory results in recent AI projects [3][7] - The article discusses the high proportion of Chinese talent within Meta's AI team, with at least half of the core members being of Chinese descent, reflecting the significant role of Chinese professionals in the American AI industry [4][10] - The article critiques the leadership of Alexander Wang from Scale AI, questioning the appropriateness of his background in data labeling for overseeing AI model development, which has raised concerns within the industry [8][9][10] Group 3 - The shift in focus from AGI (Artificial General Intelligence) to SSI (Superintelligence) in the AI discourse is noted, with both terms being described as vague and lacking clear definitions [22][24] - The article argues that the promises associated with AGI and SSI create unrealistic expectations for investment returns, complicating the financial viability of AI projects [24][25] - The emergence of Chinese open-source models, such as those from DeepSeek, is seen as a challenge to the traditional closed-source models from American companies, potentially destabilizing the market dynamics [25][30][31]