Core Insights - GPT-5 has been released, but it does not represent a significant step towards Artificial General Intelligence (AGI) [1] - The launch event revealed several issues, including presentation errors and reliance on debunked theories, which highlighted weaknesses in the Transformer architecture [1] - Despite these shortcomings, GPT-5 is still considered a competent AI product, and OpenAI plans to implement aggressive commercialization strategies in key sectors [1] Technical Development - The development of GPT-5 faced various technical bottlenecks, leading to the choice of a specific architecture to overcome these challenges [1] - The limitations of the Scaling law have been encountered, raising questions about future technological pathways for AI advancement [1] Commercial Strategy - OpenAI aims to rapidly establish a presence in three main application areas: education, healthcare, and programming [1] - The company's approach suggests a focus on leveraging GPT-5's capabilities to solidify its market position [1]

Investment Rating - The report maintains an "Overweight" rating for the technology and computer sectors [6]. Core Insights - The demand for computing power is expected to grow non-linearly due to the rise of Agentic AI, with token usage projected to increase by over 10 times, leading to a corresponding increase in computing power demand by over 100 times [1][90]. - The report highlights three scaling laws: pre-training scaling, post-training scaling, and inference scaling, which collectively indicate that the demand for computing power will continue to grow significantly [10][11]. - The relationship between token consumption and computing power demand is not linear, with a 10-fold increase in token usage potentially resulting in a 100-fold increase in required computing power [60][90]. Summary by Sections Token Demand and Computing Power - Token usage and computing power demand are expected to grow non-linearly, with the complexity of inference processes requiring significantly more computing resources as token usage increases [1][60]. - The report cites Huang Renxun's statement that a 10-fold increase in token volume could lead to a 100-fold increase in computing power requirements due to the complexity of inference processes [1][60]. Scaling Laws - The report discusses three scaling laws: pre-training scaling, post-training scaling, and inference scaling, emphasizing that the market may be underestimating the future demand for computing power due to concerns about the peak of pre-training scaling [10][11]. - Inference scaling is particularly important for improving model performance on difficult problems, which is essential for the development of Agentic AI [15][19]. Agentic AI and Token Consumption - The report identifies Deep Research as a significant driver of token consumption, with estimates suggesting that its token usage could be up to 50 times that of a single chat interaction [3][50]. - The complexity of tasks handled by Agentic AI leads to higher token consumption, with the potential for token usage to exceed 100 times that of traditional chat interactions in more complex scenarios [57][58]. Future Outlook - The report concludes that the future demand for computing power will be driven by the dual factors of increasing token usage and the complexity of inference tasks, indicating a broad space for growth in computing power demand [89][90].

Core Viewpoint - The article discusses the transformative impact of generative AI and embodied intelligence on technology, business, and society, emphasizing the need for a multi-faceted exploration of AI's opportunities and challenges [1]. Group 1: AI Development Trends - The development of AI in recent years has followed two clear trajectories: generative AI (AIGC) and embodied intelligence [5][9]. - Generative AI aims to equip machines with human-like cognitive abilities, while embodied intelligence focuses on enabling machines to mimic human sensory and action capabilities [10][11]. - The current AI landscape highlights the importance of data quality and training strategies over sheer data volume and computational power [6][19]. Group 2: Embodied Intelligence - The next phase of embodied intelligence is expected to involve mind-body coordination, reflecting the philosophical inquiry into how human-level intelligence arises [6][11]. - The application of embodied intelligence in consumer markets hinges on the machine's ability to empathize and understand human emotional needs [6][10]. - There is a significant gap in the data required for embodied intelligence to reach its potential, with current datasets lacking the scale necessary for generalization [7][24]. Group 3: AI as a Technological Revolution - Generative AI is characterized as a technological revolution based on three criteria: foundational nature, exponential productivity enhancement, and profound societal impact [13][14]. - The societal implications of AI's cognitive capabilities are vast, potentially affecting all human activities and leading to concerns about cognitive laziness among humans [14][16]. - In contrast, the impact of embodied intelligence on productivity is seen as limited compared to the cognitive advancements of generative AI [15][16]. Group 4: Data and Model Relationships - The relationship between model algorithms and data is crucial, with algorithms determining the lower limit of model performance and data defining the upper limit [20][21]. - The current focus in AI development is on enhancing data quality and training strategies, particularly in the context of embodied intelligence [19][22]. - The industry faces challenges in data acquisition for embodied intelligence, necessitating innovative approaches to data collection and synthesis [25][26]. Group 5: Future Directions - To overcome the data scarcity in embodied intelligence, strategies such as leveraging real, simulated, and synthetic data are being explored [25][26]. - The development of wearable devices capable of capturing real-world actions could provide a substantial data foundation for embodied intelligence [26]. - The complexity of human experience and environmental interaction presents significant challenges for the data-driven advancement of embodied intelligence [34][35].

Core Viewpoint - The article discusses the journey of Yang Zhilin, a prominent figure in the AI industry, highlighting the challenges faced by the younger generation of entrepreneurs in the rapidly evolving tech landscape, particularly in the context of AI 2.0 and competition with established players like DeepSeek [6][28]. Group 1: Background and Early Career - Yang Zhilin, born in 1992, was influenced by cultural icons like Haruki Murakami and Pink Floyd, which shaped his artistic and entrepreneurial aspirations [4]. - He pursued a PhD at Carnegie Mellon University, where he made significant contributions to AI, including the development of Transformer-XL and XLNet, which have been widely adopted in major AI products [9][10]. Group 2: AI Industry Landscape - The AI industry has seen a shift from mobile internet and blockchain to AI 2.0, marked by the launch of ChatGPT by OpenAI in November 2022, which has generated significant interest and investment in AI technologies [6][7]. - The 90s generation, including Yang, feels a sense of urgency to capitalize on AI as a potential opportunity for success, given their previous experiences with limited economic benefits from earlier tech trends [7][8]. Group 3: Company Development and Challenges - Yang founded "Yue Zhi An Mian" (月之暗面) in 2023, focusing on AGI (Artificial General Intelligence) and secured $200 million in initial funding from prominent investors [13][14]. - The company faced challenges, including a public relations crisis related to a reported $40 million cash-out after a $1 billion funding round led by Alibaba, which raised questions about its operational focus [14][15]. Group 4: Competition with DeepSeek - Yang's company struggled to compete with DeepSeek, founded by Liang Wenfeng, which adopted a more pragmatic approach to commercialization and technology development [13][28]. - DeepSeek's rapid success and user acquisition contrasted with Yang's strategy, which relied heavily on large-scale advertising and user data collection without significant product iteration [18][21]. Group 5: Ideological Divide - The competition between Yang and Liang represents a clash between idealism in technology development and the practical realities of business [22][23]. - Yang's focus on AGI and long-term vision may hinder immediate product development and market competitiveness, while DeepSeek's approach emphasizes rapid commercialization and user engagement [24][25]. Group 6: Future Outlook - The article suggests that despite current setbacks, opportunities still exist for Yang and other young entrepreneurs in the AI space, as the industry continues to evolve and new technological paradigms emerge [29][30]. - The narrative emphasizes the importance of balancing idealism with practical business strategies to achieve sustainable success in the competitive AI landscape [27][28].

Group 1 - Yang Zhilin, a 1992-born AI entrepreneur, has a background in music and literature, which influences his approach to technology and innovation [1][6] - He pursued a PhD at Carnegie Mellon University, where he published two significant papers, Transformer-XL and XLNet, which have been widely cited and adopted in major AI products [6][7] - After the launch of ChatGPT by OpenAI, Yang founded "The Dark Side of the Moon" (月之暗面) focusing on AGI (Artificial General Intelligence) [8][10] Group 2 - The AI landscape has evolved through various technological waves, with the current focus on AI 2.0, marked by the emergence of ChatGPT [3][4] - The competition in the AI sector is intensifying, with major players like DeepSeek gaining traction and overshadowing other startups like Yang's Kimi [18][22] - Yang's company received significant funding, including a $200 million investment from Sequoia China and ZhenFund, but faced challenges related to shareholder disputes and public scrutiny [10][12] Group 3 - The competition between Yang's Kimi and DeepSeek highlights a clash between technological idealism and commercial realism, with DeepSeek adopting a more pragmatic approach to market entry [24][28] - Kimi's user base has declined significantly, from 36 million to 18.2 million, as it struggles to keep pace with competitors [29] - Yang's focus on AGI may hinder Kimi's product iteration speed and commercial viability, as the market demands quicker adaptations [25][30] Group 4 - The AI industry is witnessing a shift towards open-source and low-cost strategies, exemplified by DeepSeek's approach, which contrasts with Kimi's more traditional methods [27][28] - The success of DeepSeek has prompted major tech companies to accelerate their AI model development, creating a more competitive environment for startups [32][34] - Despite setbacks, there remains potential for innovation and growth in the AI sector, suggesting that opportunities for Yang and his peers may still exist [36]

Core Viewpoint - Tencent has a strong inventory of chips to navigate through US chip sale restrictions and is focusing on executing its AI strategy despite the dynamic situation [1][2]. Group 1: Chip Inventory and Strategy - Tencent's president, Martin Lau, stated that the company has a "pretty strong stockpile of chips" acquired previously to manage US chip restrictions [1]. - The chips will be utilized in projects that can generate immediate returns, particularly in Tencent's advertising business [1]. - Lau emphasized that the company is exploring the right solutions to ensure its AI strategy remains executable [1]. Group 2: Training Large Language Models - Lau mentioned that Tencent will not require a large number of chips to enhance the performance of its large language models, as companies are moving away from the traditional scaling law [2]. - The company can achieve good training results with smaller clusters, indicating potential in post-training processes that do not necessitate large clusters [3]. - Tencent has enough high-end chips in its existing inventory to continue training models for several more generations [3]. Group 3: Market Context and Competitors - Nvidia announced new export licensing restrictions for chips sold to China, which may impact its inventory and financials, with a potential charge of up to $5.5 billion [4]. - Analysts believe that the new restrictions will not hinder China's AI progress, suggesting that banning the H20 chip would be counterproductive and could benefit Chinese competitors like Huawei [5].

Core Viewpoint - The article discusses the evolution of the AI foundational model landscape in China, emphasizing the rapid growth and valuation of key players in the industry, particularly following the emergence of ChatGPT. It highlights the competitive dynamics and future trends in the AI sector, particularly focusing on the "AI Six Tigers" and the impact of new entrants like Deepseek. Group 1: AI Six Tigers - The "AI Six Tigers" includes companies that have emerged rapidly since the launch of ChatGPT, with valuations exceeding 10 billion RMB, and the leading company, Zhipu, valued at over 25 billion RMB [1][6]. - Most of these companies were founded in 2023, indicating a swift response to market opportunities created by advancements in AI technology [1]. - The user base and revenue of these companies are still relatively low compared to their valuations, raising questions about their business models and sustainability [1][6]. Group 2: Key Players and Investment Dynamics - The key players in the AI sector include industry leaders, senior executives, and technical experts, many of whom have invested in multiple companies within the "AI Six Tigers" [2]. - Investment in these companies is often based on the founders' reputations and networks, reflecting a trend of "club deals" in venture capital [3]. - Recent strategic shifts among these companies include a focus on specific applications, such as healthcare for Baichuan Intelligence and multi-modal models for Minimax and Yuezhianmian [5]. Group 3: Challenges and Market Dynamics - Some companies within the "AI Six Tigers" may face financing difficulties due to high valuations, unproven business models, and questions about the scalability of their technologies [6]. - The AI industry is expected to see significant developments in 2024-2025, particularly with the emergence of major players like Deepseek [7]. Group 4: Deepseek's Impact - Deepseek has gained significant attention as a leading open-source inference model, prompting a renewed focus on foundational model research and competition in the AI sector [9]. - The success of Deepseek has encouraged more companies to open-source their foundational models, leading to advancements in multi-modal understanding and reasoning capabilities [9][10]. Group 5: Competitive Landscape - The competitive landscape for foundational models is narrowing, with key players including OpenAI, Google, and several domestic companies like Alibaba and ByteDance [12][18]. - Major companies are heavily investing in AI, with Alibaba planning to invest 380 billion RMB over three years and ByteDance over 150 billion RMB annually [12][18]. Group 6: Future Directions - The future of foundational models is expected to focus on multi-modal inputs and outputs, automation, and vertical industry applications, moving beyond simple parameter and data accumulation [22][23]. - The article suggests that the competition in AI should not be framed as a geopolitical race but rather as an opportunity for diverse innovation benefiting humanity [24].

Builders 是由 @Magineer、@有新Newin 以及 @深思SenseAI 联合推出的人物专栏，旨在发现与分享更多的优秀出海创业团队与独立开发者，我们将 定期邀请生态范围内的创始人与团队展开对话。 Tripo.AI 的迭代与未来蓝图 我们尝试基于文章内容，提出更多发散性的推演和深思，欢迎交流。 - 产品和技术能力是生成模型类公司的基本盘 ，Tripo.AI 早期从"生成快"这个点突破，在数据积累和 Scaling law 上坚持推进。 今天我们邀请到的嘉宾是 VAST CMO Sienna，邀请她和我们分享 VAST 的产品 Tripo.AI 在过去的一年时间中如何找到自己的用户，并探索用户增 长。 - 用户不断破圈很重要 ， 从AI科技爱好者，扩展到泛互联网3D设计和实体工业3D打印设计，目前也开始吸引 Roblox 平台的内容消费者。 在访谈中，我们深入探讨了目前 VAST 的产品和技术能力，拆解了目前 Tripo.AI 的核心用户画像及对应的商业模式。同时，由于 3D 内容资产天然比 较难买量传播，Tripo.AI 摸索出了一条社区运营为核心的破圈策略，Sienna 在访谈中为我们分享了很 ...