这些股票将由OpenAI现任和前任员工出售给软银等投资者。不过，目前出售计划仍处于早期洽谈阶段。 （央视财经《天下财经》）据CNBC网站16日报道，消息人士透露，美国人工智能公司OpenAI眼下正寻求按5000亿美元估值出售大约60亿美元的股权。 今年3月，OpenAI宣布完成一轮400亿美元融资，估值达到3000亿美元。 转载请注明央视财经 编辑：潘煦 ...

Core Insights - The essence of "effort" in entrepreneurship is a quantifiable economic behavior variable that plays a crucial role in value creation [2] - The concept of "effort" is linked to the economic principles of "anti-entropy" and the dynamics of capital returns [3][4] Group 1: Economic Nature of Effort - The economic nature of effort in entrepreneurship is described as "anti-entropy," countering the natural tendency of market systems towards inefficiency and resource dispersion [3] - SpaceX's efforts to reduce launch costs from approximately $150 million to $62 million per launch exemplify the successful application of effort in overcoming industry challenges [3] - The formula for great outcomes is identified as talent, practice, and effort, with SpaceX achieving a 97% rocket recovery success rate after 13 years of persistent effort [3] Group 2: Investment Strategies and Effort - In venture capital, effort manifests as a deep understanding of industry cycles, with Sequoia Capital's "zeitgeist investment method" focusing on predicting future demand gaps [4] - The investment logic aligns with the idea of creating currently missing value, where systematic effort leads to asymmetrical risk and return distributions [4] - The principle of "marginal returns" and "opportunity cost" in investment emphasizes focusing effort on critical issues rather than spreading resources thinly [4][5] Group 3: Capital Returns and Effort Density - The density of effort directly impacts capital return rates, with data showing that founders working over 60 hours a week have a 47% higher success rate in securing funding compared to those working fewer hours [5] - The concept of "effective effort zone" is introduced, highlighting the importance of matching effort with physiological limits and cognitive load [5] - Successful examples, such as ByteDance's focus on algorithm development, demonstrate how concentrated effort can lead to significant improvements in operational efficiency [5] Group 4: Creative Destruction and Industry Transformation - The theory of "creative destruction" is linked to the sustained effort required for disruptive innovation in industries, as seen in OpenAI's investment in AI model training [6] - OpenAI's investment of over $1.5 billion and the increase in training data from 10TB to 100PB illustrate the transformative potential of dedicated effort [6] - The combination of talent, practice, and effort is essential for achieving breakthroughs in technology and industry paradigms [6] Group 5: Long-term Value Creation - The long-term accumulation of knowledge and effort leads to "cognitive compounding," which is crucial for value creation in investment [8] - Historical examples, such as Warren Buffett's extensive research and reading, demonstrate how sustained effort can yield significant returns over time [8] - The emphasis is placed on recognizing and filling future value gaps through systematic effort, aligning with the principles of creating technological, market, and cognitive barriers [8] Group 6: Conclusion on Effort in Business - The narrative concludes that true greatness in business arises from persistent efforts towards unclear goals, moving away from shortcut thinking [9] - The framework of effort as a calculable and verifiable value formula is reinforced, suggesting that capital returns and industry advancements are natural outcomes of dedicated effort [9]

以下文章来源于APPSO ，作者发现明日产品的 APPSO . AI 第一新媒体，「超级个体」的灵感指南。 #AIGC #智能设备 #独特应用 #Generative AI 本文来自微信公众号： APPSO （ID：appsolution） ，作者：发现明日产品的，原文标题：《硅谷 画饼王"塌房"：奥特曼撒谎微表情被扒光，网友集体喊下台》，题图来自：视觉中国 AGI 即将到来。 我们现在有信心知道如何构建传统意义上的 AGI。 GPT-5 是一次重大升级……是通往 AGI 的重要一步。 其实 AGI 这个词没什么用。 短短半年时间内，OpenAI CEO山姆·奥特曼 （Sam Altman） 先后抛出了这个观点，第一句让全世界 振奋，第二、三句让用户和投资人躁动，第四句却又几乎否定了前面的一切。 关于AGI的定义，在他嘴里已经变成了薛定谔的猫，既存在又不存在，既重要又无关紧要。 奥特曼的人设，塌在GPT-5 尽管大家对奥特曼"营销大师"的人设早有心理预期，但GPT-5这次翻车，还是让人大跌眼镜。网友们 在对产品失望之余，还扒出了一个关于奥特曼有趣的细节。 知名学者加里·马库斯 （Gary Marcus） 在X ...

RT Internal Tech Emails (@TechEmails)Elon Musk names OpenAIDecember 3, 2015 https://t.co/uH4AnQ0Yh4 ...

Quantitative Models and Construction Methods Model: DeepSeek-R1 - **Model Construction Idea**: The DeepSeek-R1 model aims to innovate in AI technology by reducing dependency on high-end imported GPUs and enhancing cost-effectiveness and performance in global markets[5][12][30] - **Model Construction Process**: - The model is based on the DeepSeek-V3 architecture and applies reinforcement learning techniques during the post-training phase to significantly improve inference capabilities with minimal labeled data[33] - The model's performance in tasks such as mathematics, coding, and natural language inference is on par with OpenAI's o1 official version[33] - The team also introduced six distilled small models using knowledge distillation techniques, with the 32B and 70B versions surpassing OpenAI o1-mini in several capabilities[34] - The model's training cost was $5.576 million, only 1/10th of GPT-4o's training cost, and its API call cost is 1/30th of OpenAI's similar services[38] - **Formula**: $$ \text{SUE} = \frac{\text{Single Quarter Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Net Profit YoY Change over the Past 8 Quarters}} $$ where Expected Net Profit = Last Year's Same Quarter Actual Net Profit + Average YoY Change in Net Profit over the Past 8 Quarters[55] - **Model Evaluation**: The model is highly cost-effective and adaptable to different application environments, breaking the traditional AI industry's reliance on "stacking computing power and capital"[38][43] Model Backtesting Results - **DeepSeek-R1 Model**: - **AIME pass@1**: 9.3 - **AIME cons@64**: 13.4 - **MATH-500 pass@1**: 74.6 - **GPQA Diamond pass@1**: 49.9 - **LiveCodeBench pass@1**: 32.9 - **CodeForces rating**: 759.0[36] Quantitative Factors and Construction Methods Factor: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: SUE is used to measure the growth potential and latest marginal changes in the prosperity of the industry and individual stocks[57] - **Factor Construction Process**: - SUE is calculated as: $$ \text{SUE} = \frac{\text{Single Quarter Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Net Profit YoY Change over the Past 8 Quarters}} $$ where Expected Net Profit = Last Year's Same Quarter Actual Net Profit + Average YoY Change in Net Profit over the Past 8 Quarters[55] - **Factor Evaluation**: SUE effectively measures future earnings growth and the latest marginal changes in prosperity, representing the future trend changes in the industry[57] Factor Backtesting Results - **SUE Factor**: - **2022**: -29.8% - **2023**: 15.9% - **2024**: 20.1% - **2025 YTD**: 11.0%[65]

Core Insights - OpenAI is shifting its focus from solely relying on the performance of individual models like GPT-5 to a broader strategy that includes multiple consumer applications and hardware developments [1][4][7] - Despite a lukewarm reception for GPT-5, the commercial impact has been significant, with API traffic doubling within 48 hours of its launch, indicating strong market demand [2][7] Model Performance and User Experience - GPT-5 did not achieve the expected performance leap compared to its predecessor GPT-4, leading to user dissatisfaction with its response style and switching mechanism [3][4] - OpenAI executives acknowledged mistakes in the rollout of GPT-5, particularly in the transition from GPT-4o, and committed to clearer communication in future model transitions [3][4] Expansion Plans - OpenAI is planning to diversify its offerings beyond ChatGPT, with initiatives in search, consumer hardware, and enterprise software [4][5] - The company is developing AI devices in collaboration with former Apple design chief Jony Ive, and exploring new consumer applications, including a potential AI browser to compete with Google Chrome [5][6] Investment in Advanced Technologies - OpenAI intends to invest in Merge Labs, a brain-computer interface startup, positioning itself in a competitive space alongside Neuralink [6][7] - The company's ambitious plans for hardware, browsers, and advanced technologies suggest a need for substantial capital investment, leading to speculation about a potential IPO [7]

The OpenAI CEO is challenging his former friend, one company at a time. Twitter, Tesla and even Neuralink are in his sights. https://t.co/pTCDtYDsNu https://t.co/057tp8oU8U ...

Investment Rating - The report maintains a "Buy" rating for key companies in the computing power industry, specifically recommending companies like Zhongji Xuchuang, Xinyi Sheng, and Tianfu Communication [14][9][8]. Core Insights - The computing power industry is entering a phase of rapid growth, driven by significant capital expenditure from major CSPs towards AI computing power [24][3]. - The macroeconomic environment, particularly the strong expectations for interest rate cuts in North America, is expected to enhance the long-term value of growth stocks, particularly in the tech sector [25]. - AI applications are reaching a profitability inflection point, with leading companies leveraging their advantages to penetrate vertical markets [26][2]. - The demand for computing power is becoming increasingly critical, with major companies like Meta and OpenAI planning substantial investments in data center infrastructure [27][3]. - The industry is characterized by a "stronger getting stronger" dynamic, with established players solidifying their market positions through technological advantages and deep customer relationships [29][7]. Summary by Sections Macroeconomic Perspective - The expectation of interest rate cuts in the U.S. is likely to reduce debt costs for AI companies, alleviating financial pressure and encouraging further investment in R&D and acquisitions [25][24]. Mid-level Perspective - AI applications are accelerating in both technological advancements and user adoption, with significant growth in user numbers for platforms like GPT [26][2]. - The profitability of AI applications is transitioning from experimental phases to established business models, with major players expanding into new verticals [26][2]. Micro-level Perspective - The computing power market is witnessing a solid oligopoly, with domestic companies like Zhongji Xuchuang and Xinyi Sheng gaining a competitive edge through specialized technology and long-term partnerships with overseas clients [29][7]. - Innovations in computing infrastructure, particularly in optical communication and liquid cooling technologies, are expected to enhance efficiency and performance [29][8]. Investment Recommendations - The report recommends focusing on leading companies in the computing power supply chain, including Zhongji Xuchuang, Xinyi Sheng, and Tianfu Communication, as well as companies involved in liquid cooling solutions [9][8][29].

Core Viewpoint - The article discusses the ongoing legal battles surrounding AI companies and their use of copyrighted materials for training large models, highlighting the shift in focus from how data is used to how it is obtained [8][19]. Group 1: Legal Battles and Implications - In 2023, lawsuits against OpenAI and Microsoft initiated a wave of legal challenges in Silicon Valley, with major players like Meta and Anthropic also facing litigation for using copyrighted materials without authorization [8][9]. - The core issue revolves around whether the use of copyrighted works for AI training constitutes "transformative use" or "infringement" [8][19]. - A significant ruling in the Anthropic case indicated that while the training process may be transformative, the means of obtaining data, especially if involving piracy, is unlikely to be protected under fair use [9][19]. Group 2: Data Acquisition Methods - AI companies have employed various controversial methods to gather training data, often skirting legal boundaries [10]. - The initial method involved indiscriminate web scraping of publicly available content, which included copyrighted materials [11]. - A more severe issue arose when companies like OpenAI were accused of systematically removing copyright management information during data collection, indicating a deliberate intent to evade copyright laws [12]. Group 3: Innovative Yet Risky Techniques - As the availability of high-quality public data dwindled, companies began converting other formats, such as videos and books, into text for training purposes [13]. - OpenAI reportedly transcribed over one million hours of YouTube content using its Whisper tool, raising concerns over copyright infringement [13]. - Anthropic's approach involved purchasing physical books, scanning them, and then destroying the originals to argue that this was a legal format conversion rather than creating unauthorized copies [14]. Group 4: The Shadow Library and User Data - Some companies opted for high-risk strategies by directly utilizing resources from illegal libraries, such as "Library Genesis" [16]. - Others, like Google, leveraged user-generated content through privacy agreements, effectively internalizing user data for AI training without external scraping [17]. Group 5: Industry Transformation and Future Costs - The shift in litigation focus has transformed copyright holders from passive victims to key players with significant bargaining power in the AI industry [21]. - As AI companies face increasing legal scrutiny, the cost of acquiring compliant data is expected to rise significantly, marking the end of the "free data" era [20][21]. - The competition in the AI sector is evolving from purely algorithmic and computational prowess to include data supply chain management and legal compliance capabilities [21].

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The impact of AI is not limited to software; various sectors are witnessing the rise of software companies seizing opportunities in the AI era, such as Applovin in advertising and Figma and Canva in visual design [1][15] - Companies with strong know-how, proprietary data, complex processes, or regulatory barriers are less likely to be disrupted by large models; instead, these models may enhance their competitive advantages [2][20] - The development of open-source models is beneficial for software companies, allowing them to develop independently or negotiate better with closed-source models [19] Industry Trends - The report highlights a significant growth in AI-related revenues, with OpenAI's annual recurring revenue surpassing $13 billion and Anthropic's revenue reaching $4 billion, a fourfold increase since the beginning of the year [12] - Concerns about AI disrupting software have led to stock declines in companies like Adobe (down 23%) and ManpowerGroup (down 30%) [14] - The report identifies three types of AI agents: user-created agents, vendor-provided agents, and enterprise-deployed agents, indicating a shift towards personalized and automated solutions [3][37] Recommendations - The report suggests focusing on companies involved in computing power, such as Cambrian, Hygon Information, and others, as well as those developing AI agents like Alibaba and Tencent [7][53] - It also mentions companies in the autonomous driving sector, including Jianghuai Automobile and Xiaopeng Motors, as potential investment opportunities [54]