Core Viewpoint - The meeting between NVIDIA's CEO Jensen Huang and Xiaomi's Lei Jun focused on AI topics, highlighting the significant opportunities AI presents for both China and the U.S. [1] Group 1: AI and Market Developments - Jensen Huang expressed admiration for Xiaomi's SU7 Ultra model, indicating a positive view of Xiaomi's automotive efforts [1] - NVIDIA has received U.S. approval to resume sales of the H20 chip in China, which is designed specifically for the Chinese market [1][2] - Huang mentioned that there are many orders for the H20 chip, with some companies already awaiting formal shipping notifications from NVIDIA [1] Group 2: AI Industry Recognition - Huang acknowledged the contributions of Chinese AI companies such as DeepSeek, Tencent, Alibaba, MiniMax, and Baidu, stating that their models have advanced global AI development [1]

Group 1 - CEO Jensen Huang praised China's AI models as "world-class" and emphasized the importance of the Chinese market for NVIDIA, committing to continued investment in the region [1] - Huang highlighted that AI will revolutionize traditional manufacturing by enabling collaboration between humans and AI, leading to a new industrial revolution and growth opportunities [1] - NVIDIA is closely collaborating with Xiaomi in multiple fields, and Huang expressed strong interest in Xiaomi's electric vehicles, noting the impressive advancements in China's electric vehicle sector over the past five years [1] Group 2 - Huang stated that AI has become a new infrastructure, comparable to electricity and the internet, reshaping global supply chains and altering the design, production, and transportation of goods [2] - He acknowledged the rapid development of AI in China, specifically mentioning companies like Tencent, NetEase, and Alibaba, and noted that over 1.5 million developers in China are using NVIDIA technology to drive innovation [2] - Huang praised the open-source model adopted by Chinese companies, which fosters international cooperation and the establishment of AI safety standards, citing the Kimi K2 model as an example of surpassing OpenAI's ChatGPT [2] Group 3 - AI is transforming every industry and driving various sectors of Chinese consumer technology, including platforms like WeChat, Taobao, and Meituan, all of which rely on AI [3] - NVIDIA is preparing to resume shipments of the H20 chip to China, which had been halted due to U.S. export restrictions, significantly impacting NVIDIA's market share in China [3] - Following the announcement, NVIDIA's stock price rose by 4.04%, reaching $170.7, with a market capitalization increase of $161.8 billion (approximately 1160.5 billion RMB) [3]

Group 1 - The core viewpoint is that AI and software will drive factories in the next decade, creating new opportunities for China's supply chain ecosystem [1][3] - NVIDIA's CEO Jensen Huang highlighted the significance of China's supply chain, calling it a miracle and emphasizing the role of AI in transforming manufacturing processes [3][4] - NVIDIA has evolved from a gaming chip company to a provider of foundational infrastructure for AI, indicating a major shift in the industry [4] Group 2 - Huang noted that AI has enhanced computational capabilities by 100 times compared to previous architectures, significantly outpacing Moore's Law [3] - The company is focused on building a global AI ecosystem, with applications ranging from healthcare to transportation, showcasing the versatility of AI technology [3][4] - Over 1.5 million developers in China are currently utilizing NVIDIA's platform for AI development, indicating a robust ecosystem of innovation [3]

7月16日，英伟达创始人、CEO黄仁勋现身链博会开幕式。他在演讲中，提及了腾讯、网易、米哈 游、字节跳动、DeepSeek、阿里巴巴、MiniMax、百度、小米等中国公司。 他表示，中国超快的创新能力由研究员和企业家创造，像DeepSeek、腾讯、阿里巴巴、MiniMax、 百度等模型的共享推动了全球人工智能的发展和进步。 ...

Core Viewpoint - The article discusses the competitive landscape of AI models, particularly focusing on DeepSeek and its challenges in maintaining user engagement and market position against emerging competitors like Kimi and others in the "AI Six Dragons" group. Group 1: DeepSeek's Performance and Challenges - DeepSeek experienced a significant decline in monthly active users, dropping from a peak of 169 million in January to a decrease of 5.1% by May [1][2]. - The download ranking of DeepSeek has plummeted, moving from the top of the App Store charts to outside the top 30 [2]. - The user engagement rate for DeepSeek has fallen from 7.5% at the beginning of the year to 3% by the end of May, with a 29% decrease in website traffic [2][3]. Group 2: Competition and Market Dynamics - Competitors like Kimi and others are rapidly releasing new models, with Kimi K2 achieving significant performance benchmarks and offering competitive pricing [1][8]. - The pricing strategy of Kimi K2 aligns closely with DeepSeek's API pricing, making it a direct competitor in terms of cost [8]. - Other players in the market are also emphasizing lower costs and better performance, which is eroding DeepSeek's previously established reputation for cost-effectiveness [7][8]. Group 3: Technological and Strategic Implications - DeepSeek's reliance on the H20 chip has been impacted by export restrictions, which has hindered its ability to scale and innovate [3][4]. - The lack of major updates to DeepSeek's models has led to a perception of stagnation, while competitors are rapidly iterating and improving their offerings [6][12]. - The article highlights the importance of multi-modal capabilities, which DeepSeek currently lacks, potentially limiting its appeal in a market that increasingly values such features [13]. Group 4: Future Outlook - To regain market interest, DeepSeek needs to expedite the release of new models like V4 and R2, as well as enhance its tool capabilities to meet developer needs [12][13]. - The competitive landscape is shifting rapidly, and without significant updates or innovations, DeepSeek risks losing further ground to its rivals [12][14]. - The article suggests that maintaining developer engagement and user interest is crucial for DeepSeek's long-term success in the evolving AI market [11].

国芯网[原:中国半导体论坛] 振兴国产半导体产业！ 不拘中国、 放眼世界 ！ 关注 世界半导体论坛 ↓ ↓ ↓ 7月15日消息，据报道，英伟达黄仁勋宣布 ：美国已批准H20芯片销往中国！ 黄仁勋表示："美国政府已经批准了我们的出口许可，我们可以开始发货了，所以我们将开始向中国市 场销售H20。我非常期待能很快发货H20，对此我感到非常高兴，这真是个非常、非常好的消息。第二 个消息是，我们还将发布一款名为RTX Pro的新显卡。这款显卡非常重要，因为它是专为计算机图形、 数字孪生和人工智能设计的。" H20芯片是英伟达为满足美国此前出口管制，而在2023年底推出专供中国市场的减配AI加速器。其基于 Hopper架构，算力仅为旗舰产品H100的六分之一。 时至今年年初，H20伴随国内AI算法公司DeepSeek横空出世而大放异彩。DeepSeek通过算法改进大幅降 低训练和推理成本，而业内此时也关注到，H20通信带宽速率及显存容量甚至优于A100，同时其价格较 低，高带宽与集群部署能力让这款算力芯片成为最适配DeepSeek本地部署的硬件方案。 以国内某服务器厂商的企业级方案，其仅用1台FusionServer ...

Core Viewpoint - DeepSeek is experiencing a decline in user engagement and website traffic, yet remains committed to its long-term vision of developing an ecosystem for general artificial intelligence (AGI) [3][4][5]. User Engagement and Traffic - DeepSeek's user usage rate has dropped from a peak of 7.5% at the beginning of the year to 3% [3]. - As of May, DeepSeek's mobile monthly active users decreased to 169 million from 194 million in March, a drop of 25 million [3]. Company Vision and Strategy - Founder Liang Wenfeng emphasizes that the current phase is one of technological innovation rather than application explosion, aiming to establish an ecosystem for direct industry use of their technology [4]. - The transition from a technology acceleration phase to an application acceleration phase is anticipated around 2025, as noted by industry expert Zhou Zhifeng [4]. Competitive Landscape - DeepSeek is compared to OpenAI, possessing strong foundational models and the largest user base among AI products in China, but faces competition from tech giants like Alibaba and Tencent [4]. - OpenAI has advanced in application-level organization, appointing a dedicated "application CEO" to focus on product and business development [5]. Product Development and Updates - DeepSeek has made recent updates to its product, including new login options and functionality enhancements, such as text recognition and file upload features [8][9]. - The service uptime for DeepSeek's API and web chat services has exceeded 99% in the last 90 days, resolving previous performance issues [9]. Recruitment and Team Composition - DeepSeek is actively recruiting for product and design roles, indicating a focus on developing next-generation intelligent products centered around large language models (LLM) [11]. - The team consists of approximately 130 members, primarily young graduates from domestic universities, characterized by their passion for technology [11]. Market Position and Future Outlook - For AI startups, creating a super consumer application is crucial for securing funding and ensuring growth, as many are transitioning from startup to growth stages [13]. - DeepSeek is positioned to leverage its backing from Huanfang Quantitative, providing a financial safety net for its commercial endeavors [14]. - The ultimate goal for DeepSeek is to establish a comprehensive AI ecosystem, akin to those developed by major companies like Google and Apple, although this is recognized as a long-term endeavor [15][17].

Core Viewpoint - Meta is considering a significant shift in its AI strategy by potentially moving from open-source AI models to closed-source models, which could mark a departure from its long-standing commitment to open-source development [1][5][6] Group 1: Strategic Shift - Meta's newly established "Super Intelligence Lab" (MSL) is contemplating abandoning its powerful open-source AI model, Behemoth, in favor of developing a closed-source model [1][5] - This potential shift is seen as a major strategic change for Meta, which has historically believed that open-source technology fosters faster AI development and broader access for developers [5][6] - The decision is reportedly influenced by the underperformance of the Behemoth model during internal testing, leading to delays in its release [5][6] Group 2: Leadership and Talent Acquisition - Meta has appointed Alexandr Wang, the new AI head, who previously led Scale AI, to oversee the Super Intelligence Lab, which consists of a specialized team of about 12 members [6][7] - The company has adopted a "high-paying talent acquisition" strategy, offering salaries exceeding $100 million to attract top researchers from competitors like OpenAI, Google, and Apple [5][6] Group 3: Market Implications - The shift towards closed-source models could signify a retreat from the competitive landscape of open-source large language models (LLMs), with concerns raised about the U.S. losing its edge in this area [1][3] - The ongoing developments in Meta's AI strategy are closely watched, especially as the company faces challenges in the AI technology sector [5][6]

Core Insights - DeepSeek's user engagement has significantly declined, with usage rates dropping from a peak of 7.5% at the beginning of the year to 3% currently [2] - The anticipated launch of the new model, DeepSeek-R2, has been delayed multiple times, contributing to the decrease in user interest and engagement [2] - Competitors like ChatGPT and Google Gemini have seen substantial growth in website traffic, with increases of 40.6% and 85.8% respectively during the same period [3] Usage Statistics - DeepSeek's usage rate fell from 7.5% in early January to 3% now, indicating a significant drop in user engagement [2] - The usage rate of DeepSeek R1 also halved from 7% in February to 3% by the end of April [2] - The share of token traffic hosted on third-party platforms dropped from 42% in March to 16% in May [2] Model Development and Competition - The delay in the release of DeepSeek-R2 is attributed to the CEO's dissatisfaction with the model's performance, leading to ongoing internal enhancements [2] - The shortage of NVIDIA H20 chips has impacted both the release of the new model and the deployment of existing models [2] - Despite DeepSeek's challenges, competitors are actively innovating and gaining market share [3] Data Limitations - There are concerns regarding the limitations of the data from Semianalysis and Poe, particularly in relation to the Chinese market and the scope of their coverage [3] - Poe's usage data is based solely on its subscribers and does not account for third-party integrations with DeepSeek, such as Tencent and Baidu [3]

Model Overview - Kimmy K2 is a state-of-the-art mixture of experts language model with 32 billion activated parameters and 1 trillion total parameters [3] - The model was pre-trained on 155% trillion tokens with zero training instability [4] - Kimmy K2 supports up to 2 million tokens in the context window [5] Performance Benchmarks - Kimmy K2 Instruct beats Deepseek, Quen, and GPT41 on SWEBench verified, coming in right behind Cloud 4 Opus [7] - On Live Codebench, Kimmy K2 beats Cloud 4 Opus [7] - Kimmy K2 tops the list on Amy 2025 for math, GPQA Diamond [8] Optimization and Training - The model is trained with the Muon optimizer [4] - Kimmy K2 achieves exceptional performance across frontier knowledge reasoning and coding tasks [4] - The training process was open source [8] Availability and Cost - Inference is available through Kimmy directly at $0.15 per million input tokens with a cache, $0.60 without a cache, and $2.50 per million output tokens [10] - Kimmy K2 is available on Open Router [13] Industry Reception - Industry experts compare Kimmy K2 to Deep Seek V3 [11] - Kimmy K2 is recognized as a potentially new leader in open LLMs [14]