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B端战场的AI叙事:一场极致的效率和场景争夺战|AI观察系列策划②
Mei Ri Jing Ji Xin Wen· 2025-10-09 11:05
B端战场的AI( 人工智能 )叙事正持续升温。 一位押中"杭州六小龙"的投资人近日向《每日经济新闻》记者(以下简称"每经记者")透露:"目前AI领域的投资,基本以看B端的AI项目为主,核心考量的 是商业化闭环和变现能力。" 在评估AI商业化的竞赛中,一个关键指标正被推至台前:Token(计算机领域多场景使用的数字标识符)。这一指标正如同 互联网 时代的"用户数",成为衡 量AI公司实际采用规模与增长潜力的核心标尺。而眼下,一个正在发生的事实是,Token的调用量正快速增长。 在9月底举行的云栖大会上,谈及AI在各行业的应用, 阿里云 智能集团通义大模型业务总经理徐栋向包括每经记者在内的媒体表示:"在一年前,很多模型 的调用量都是来自于离线的打标,特定的一些娱乐性的场景会多一点。但最近半年,很明显的变化是,在线任务大幅上升,不少互联网公司、 消费电子 企 业的大部分交互已经开始由大模型来取代了。" 另一方面,AI技术本身也在进化,从单一的大语言模型向多模态演进,业内对AI的讨论也从生成式大模型向 空间计算 进化,从Copilot(助手)切换到 Agent(智能体),从纯 软件 端向软硬件一体摸索。 AI技术带来 ...
“芯片+应用”双引擎,拥抱人工智能广阔前景
Mei Ri Jing Ji Xin Wen· 2025-09-23 02:02
当然对国内来讲非常重要的就是,一方面从模型层面上来讲,国内的DeepSeek-R1,包括像字节豆包等 大模型从技术实力或者从表现上来讲也在不断追赶海外的头部大模型,另一方面国产的GPU市场还是 有比较巨大的投资机会。 有一些国产的算力产业链标的,前几年还处在业绩亏损的状态,但是在今年市值出现了比较明显的增 长,或者说股价涨幅会比它短期的业绩释放更早。所以大家就会有很多疑问,是不是这里面会存在一些 涨幅过高或者说估值过高的问题。但从产业趋势层面上来讲,包括我们去看市场空间层面来讲,我们觉 得国产算力或者说国产GPU层面还是有比较巨大的投资机会的。 首先我们看到,今年在英伟达二季度的财报说明会里,黄仁勋最新的表述也提到,整个国内能够提供的 算力芯片市场就能够达到500亿美金的体量,并且未来每年还会有差不多30%的年均复合增速。算力芯 片市场折算下来差不多就是3000多亿人民币到4000亿人民币的体量,这个体量我觉得相对还是比较庞大 的,并且未来每年还能够会有30%的增速。 但是在今年我们看到,一方面是由于海外的制裁,另一方面国内也在积极推进算力芯片,包括GPU,包 括国产的ASIC渗透率提升等等。所以未来几年我们 ...
具身领域的大模型基础部分,都在这里了......
具身智能之心· 2025-09-20 16:03
Core Viewpoint - The article emphasizes the importance of a comprehensive community for learning and sharing knowledge about large models, particularly in the fields of embodied AI and autonomous driving, highlighting the establishment of the "Large Model Heart Tech Knowledge Planet" as a platform for collaboration and technical exchange [1][3]. Group 1: Community and Learning Resources - The "Large Model Heart Tech" community aims to provide a platform for technical exchange related to large models, inviting experts from renowned universities and leading companies in the field [3][67]. - The community offers a detailed learning roadmap for various aspects of large models, including RAG, AI Agents, and multimodal models, making it suitable for beginners and advanced learners [4][43]. - Members can access a wealth of resources, including academic progress, industrial applications, job recommendations, and networking opportunities with industry leaders [7][70]. Group 2: Technical Roadmaps - The community has outlined specific learning paths for RAG, AI Agents, and multimodal large models, detailing subfields and applications to facilitate systematic learning [9][43]. - For RAG, the community provides resources on various subfields such as Graph RAG, Knowledge-Oriented RAG, and applications in AIGC [10][23]. - The AI Agent section includes comprehensive introductions, evaluations, and advancements in areas like multi-agent systems and self-evolving agents [25][39]. Group 3: Future Plans and Engagement - The community plans to host live sessions with industry experts, allowing members to engage with leading figures in academia and industry [66]. - There is a focus on job sharing and recruitment information to empower members in their career pursuits within the large model domain [70].
真的花了好久才汇总的大模型技术路线......
具身智能之心· 2025-09-16 00:03
Core Insights - The article emphasizes the transformative impact of large models on various industries, highlighting their role in enhancing productivity and driving innovation in fields such as autonomous driving, embodied intelligence, and generative AI [2][4]. Group 1: Large Model Technology Trends - The large model industry is undergoing significant changes characterized by technological democratization, vertical application, and open-source ecosystems [2]. - There is a growing demand for talent skilled in technologies like RAG (Retrieval-Augmented Generation) and AI Agents, which are becoming core competencies for AI practitioners [2][4]. - The article introduces a comprehensive learning community focused on large models, offering resources such as videos, articles, learning paths, and job exchange opportunities [2][4]. Group 2: Learning Pathways - The community provides detailed learning pathways for various aspects of large models, including RAG, AI Agents, and multimodal models [4][5]. - Specific learning routes include Graph RAG, Knowledge-Oriented RAG, and Reasoning RAG, among others, aimed at both beginners and advanced learners [4][5]. - The pathways are designed to facilitate systematic learning and networking among peers in the field [5]. Group 3: Community Benefits - Joining the community offers benefits such as access to the latest academic advancements, industrial applications, and job opportunities in the large model sector [7][9]. - The community aims to create a collaborative environment for knowledge sharing and professional networking [7][9]. - There are plans for live sessions with industry leaders to further enrich the community's offerings [65][66].
中国GenAI市场洞察:企业级大模型调用全景研究
Tou Bao Yan Jiu Yuan· 2025-09-03 12:31
Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The Chinese enterprise-level GenAI market is experiencing explosive growth, with daily model invocation reaching 101,865 billion tokens in the first half of 2025, a 363% increase from 21,999 billion tokens in the second half of 2024 [8][18][11] - The market is transitioning towards a dual-track development of open-source and closed-source models, with open-source models gaining traction due to their cost-effectiveness and flexibility [13][16] - The focus of enterprise-level model application is shifting from seeking a single powerful model to finding optimal solutions tailored for specific business scenarios, emphasizing cost-performance ratio, system flexibility, and security [6][20] Summary by Sections Introduction - The report, published by Frost & Sullivan in collaboration with the Head Leopard Research Institute, surveys 700 IT department heads, technical directors/managers, and AI project leaders across various industries including finance, manufacturing, internet, consumer electronics, and automotive [4][28] - The study aims to assess the deployment of open-source and closed-source models in the enterprise-level GenAI market and to provide structured insights into the current application status and trends [4] Section 1: Overview of Enterprise-Level GenAI Development - The development of enterprise-level GenAI is characterized by the parallel growth of open-source and closed-source models, with open-source models becoming the preferred choice for low-cost implementation and autonomy [13][16] - Open-source models are increasingly recognized for their adaptability and long-term value, while closed-source models are favored for their reliability and performance [13][16] Section 2: Current Status and Trends of Model Invocation - The daily invocation of enterprise-level models has surged, indicating a shift from pilot testing to large-scale implementation, with significant implications for resource consumption and industry restructuring [18][19] - Key drivers of this growth include the expansion of model and computing power supply, accelerated deployment in various sectors, and the emergence of ecosystem effects that enhance efficiency [19][20] Section 3: Analysis of Model Invocation Behavior - The choice between open-source and closed-source models is primarily driven by business value, with open-source models offering greater flexibility and control, while closed-source models provide reliability and ease of use [24][26] - The top factors influencing the selection of open-source models include performance, customization ease, and knowledge ownership, whereas closed-source models are chosen for their reliability and brand reputation [25][26][27]
中国大模型企业级市场爆发增长 调用大模型日均消耗激增
Zhong Guo Xin Wen Wang· 2025-09-03 08:48
Core Insights - The generative AI market in China is experiencing explosive growth, with a projected daily usage increase of 363% from the second half of 2024 to the first half of 2025, exceeding 10 trillion tokens [1] - Alibaba's Tongyi model leads the market with a 17.7% share, followed by ByteDance's Doubao at 14.1% and DeepSeek at 10.3%, collectively accounting for over 40% of the market [1] - The Chinese government is actively promoting the integration of AI into various industries, aiming to enhance traditional sectors and foster new strategic industries [2] Market Trends - Public cloud deployment of large models is becoming mainstream, with 70% of enterprises opting for this approach, and 71% planning to increase their use of generative AI services in the cloud [2] - There is a shift from seeking the single strongest model to finding optimal solutions for specific business scenarios, indicating a growing demand for diverse model types and applications [2] - Open-source models are emerging as a key growth driver in the enterprise market, with predictions that over 80% of enterprises will adopt open-source large models in the future [3] Technological Advancements - The Tongyi Qwen model has become the largest open-source model family globally, with over 400 million downloads and more than 140,000 derivative models [3] - Continuous advancements in large language models, reasoning capabilities, and multi-modal models are reshaping the AI landscape in China [3][4] - The focus is shifting from merely generating content to executing tasks, marking a significant transition towards general intelligence in AI [5]
DeepSeek等大模型集体“打标”,从此告别AI造假?
虎嗅APP· 2025-09-02 14:00
Core Viewpoint - The article discusses the implementation of the "Artificial Intelligence Generated Content Identification Measures," which mandates that all AI-generated content must be clearly labeled to protect users, especially those with limited discernment abilities, from misinformation and deception [8][44][65]. Group 1: AI Content Identification - Starting September 1, the "Artificial Intelligence Generated Content Identification Measures" requires all AI-generated content to be labeled, ensuring transparency [8]. - Major AI model companies like Tencent, ByteDance, and Alibaba have already begun updating their user agreements to comply with AI content labeling [6][7]. - The measures apply to various forms of content, including text, images, audio, and video, and require both service providers and users to adhere to labeling protocols [9][10]. Group 2: Impact on Users - The article highlights the growing concern over the ability of users, particularly the elderly, to discern AI-generated content from real content, leading to potential emotional and financial exploitation [16][22]. - Examples are provided where individuals were misled by AI-generated videos, illustrating the risks associated with the lack of clear identification [18][20]. - The introduction of AI content labels is seen as a necessary step to protect vulnerable groups from being misled by AI-generated misinformation [22][43]. Group 3: Global Context and Challenges - The article compares the new measures in China with similar regulations in countries like South Korea and Spain, noting that the U.S. lacks comprehensive federal regulations on AI content labeling [45][46]. - The challenges of enforcing AI content identification are acknowledged, with concerns that voluntary compliance by tech companies may not be sufficient to address the proliferation of misleading AI content [47][61]. - The article cites data indicating that human influencers earn significantly more than AI-generated content creators, highlighting the ongoing struggle for authenticity in the creator economy [63].
DeepSeek等大模型集体“打标”,从此告别AI造假?
Hu Xiu· 2025-09-02 09:12
Core Viewpoint - The implementation of the "AI-generated content identification method" aims to ensure that all AI-generated content is clearly marked, enhancing transparency and protecting users from misinformation [7][30][51]. Group 1: Regulatory Developments - On September 1, the "Identification Method for AI-generated Synthetic Content" officially took effect, requiring all AI-generated content to be clearly identified [7]. - Major AI model companies, including Tencent and ByteDance, have updated their user agreements to comply with the new identification requirements [4]. - The regulation mandates that AIGC service providers, platforms, and users must adhere to both explicit and implicit identification of AI content [8][9][10]. Group 2: Impact on Users - The introduction of AI content identification is seen as a protective measure for users, particularly those with limited ability to discern AI-generated content from real content [30]. - There are concerns that even tech-savvy individuals may struggle to differentiate between AI-generated and real videos, leading to potential misinformation [41][49]. - Examples of misinformation due to AI content include elderly individuals being misled by AI-generated videos, highlighting the need for clear identification [23][24][30]. Group 3: Industry Response - Various internet platforms, such as Bilibili and Douyin, have introduced features allowing users to declare AI content, aligning with the new regulations [12]. - The AI content landscape is rapidly evolving, with a significant increase in AI-generated videos, raising concerns about the impact on human creators and the authenticity of content [61][80]. - The creator economy is projected to grow significantly, with AI-generated content becoming a substantial part of the market, indicating a shift in content creation dynamics [80].
DeepSeek 等大模型集体“打标”,从此告别 AI 造假?
3 6 Ke· 2025-09-02 08:00
Core Viewpoint - The implementation of the "AI-generated content identification method" aims to ensure that all AI-generated content is clearly marked, enhancing transparency and protecting users from misinformation [7][18][45]. Group 1: Regulatory Developments - On September 1, the "Identification Method for AI-generated Synthetic Content" officially took effect, requiring all AI-generated content to be clearly identified [7]. - Major AI model companies, including Tencent and ByteDance, have updated their user agreements to comply with the new identification requirements [4]. - The regulation mandates that AI content creators, platforms, and users must adhere to explicit and implicit labeling of AI-generated content [7]. Group 2: Industry Response - Various internet platforms, such as Bilibili, Douyin, and Kuaishou, have introduced features allowing users to declare AI content, accompanied by platform identification [8]. - The rise of AI content has led to concerns about its authenticity, with users increasingly unable to distinguish between real and AI-generated content [9][28]. Group 3: User Impact and Concerns - The proliferation of AI content has raised alarms, particularly among vulnerable groups like the elderly, who may be easily misled by AI-generated materials [18]. - Examples of misinformation include elderly individuals believing in AI-generated videos that misrepresent reality, leading to potential emotional and financial consequences [14][15]. - Young users also face challenges, as they may become victims of AI-generated content, such as manipulated videos used for social pressure [19][24]. Group 4: Global Context - The regulatory approach in China is noted to be more stringent compared to other countries, with similar initiatives emerging in South Korea and Spain, while the EU is working on a broader AI regulation [33][35]. - The lack of federal regulations in the U.S. contrasts with the mandatory measures in China, raising questions about the effectiveness of voluntary compliance by tech companies [33][40]. Group 5: Market Trends - The creator economy, including AI-generated content, is projected to grow significantly, with estimates suggesting it could reach $25 billion by 2025, up from $16.4 billion in 2022 [44]. - Despite the growth of AI content, human creators still earn significantly more, with AI influencers earning only 46% of what human influencers make [44].
中国企业大模型日均调用量破10万亿Tokens,通义豆包DeepSeek领跑市场
Sou Hu Cai Jing· 2025-09-01 09:05
Core Insights - The Frost & Sullivan report highlights the rapid growth and adoption of generative AI in the Chinese enterprise market, with an astonishing daily consumption of 10.2 trillion tokens expected by the first half of 2025 [1] - The report indicates a significant increase of 363% in daily model invocation compared to the second half of 2024, surpassing the 10 trillion tokens mark [1] - Leading platforms in this market include Alibaba Tongyi, ByteDance Doubao, and DeepSeek, which collectively hold over 40% market share [1] Industry Trends - Public cloud has emerged as the preferred method for Chinese enterprises to deploy and invoke large models, with 70% of companies favoring this approach [3] - A notable 71% of enterprises plan to further increase their use of generative AI services in public cloud environments, indicating a shift towards seeking optimal solutions for specific business scenarios [3] - The rise of open-source models is becoming a key driver for market growth, with a significant reduction in performance gaps between domestic open-source models and top international closed-source models [3] Future Projections - It is predicted that over 80% of enterprises will adopt open-source large models, suggesting that open-source solutions will dominate enterprise-level applications in the future [3]