马斯克笑了，孙正义慌了。 5月6日，OpenAI宣布取消经营主体从非营利性组织法人转为营利AI企业的计划，未来，公司将回归非营利组织主导的模式，放弃全面营利性转型。在"非 营利控股+公益企业"双轨治理架构下，OpenAI发生了"去商业化"的重大转向，在全球范围内引发对技术伦理、资本逻辑与公共利益平衡的深度讨论。 根据OpenAI的介绍，公司的架构主要有四大方面：一是将继续由目前的非营利组织控制；二是现有的营利部门将成为公共利益公司(PBC)；三是非营利组 织将控制PBC，并成为其大股东；四是非营利组织和PBC将继续保持相同的使命。 这一声明意味着，OpenAI将继续保持与目前"极其接近"的结构。外界分析称，OpenAI放弃全面营利性转型或是多重压力的下的无奈妥协。自开启营利性 转型以来，OpenAI不仅受到了特斯拉创始人埃隆.马斯克源源不断的诉讼指控，还受到了来自政府监管机构的高度关注与介入调查。 但随着AI研究的推进，公司需要的计算资源及高素质人才与日俱增。为了获得更多投资，OpenAI在2019年创建了一个营利性子公司"OpenAI LP"，隶属于 非营利组织之下，用以筹集投资资金并以初创公司的股权吸引人 ...

Core Insights - The competition among digital chain leaders is inherently global, driven by the rapid advancement of AI and smart technologies, which are disrupting traditional chain leaders [2][3] - Digital and intelligent transformation is becoming a new trend in global production networks, with the potential to revolutionize human production and lifestyle [2][3] - The emergence of digital chain leaders, or "smart chain leaders," is crucial as they integrate material and data through AI, enhancing production capabilities and decision-making intelligence [3][5] Group 1: Impact of AI on Traditional Chain Leaders - The acceleration of intelligent transformation is leading to the replacement of traditional chain leaders, with smart chain leaders striving to be the first to achieve large-scale AI practical application [5][6] - The historical context shows that once AI surpasses certain thresholds, it can lead to disruptive changes across industries, as seen in examples like the evolution of Go and the automation of parking systems [6][7] - The urgency for businesses to embrace AI is palpable, with a growing anxiety among entrepreneurs to understand and leverage AI technologies [7][8] Group 2: Differentiation Between Digitalization and Intelligentization - Digitalization is recognized for its potential to enhance efficiency, but its benefits are often indirect and limited, while intelligentization can dramatically improve production efficiency [8][9] - The competition among smart chain leaders is global, as breakthroughs in intelligentization can lead to significant productivity gains, posing existential threats to traditional chain leaders [8][9] Group 3: Technical Routes and Responsibilities of Smart Chain Leaders - The debate over AI's development routes—AI hegemony versus AI equality—highlights the importance of smart chain leaders in driving industry-specific AI applications [9][10] - Smart chain leaders must undertake deep digitalization to align with intelligentization needs, moving beyond superficial digital efforts to detailed process digitization [12][13] - They also need to adapt to rapid AI iterations, engaging in a continuous learning process to remain competitive [13][14] Group 4: Long-term Process of Societal Digitalization - The journey towards societal digitalization is expected to be lengthy, with significant industry reshuffling akin to the impact of the internet on various sectors [15] - The development of general artificial intelligence (AGI) and industry-specific AI applications are critical areas for future focus, requiring collaboration among industry players to establish smart chain leaders [15]

四是科创资源聚集，多项指标"领跑"。天津经开区全社会R&D投入持续保持全市首位；技术市场活跃 度显著提升，一季度技术合同交易额突破40亿元，同比增长10%，持续领跑全市；创新人才引育成效显 著，5人入选滨海新区第四批杰出人才培养计划，入选人数居新区首位。 天津经开区相关负责人表示，一季度亮眼成绩是新起点，天津经开区将乘势而上，持续深化科技创新工 作，在未来产业发展、科技金融、创新平台建设等多个方面持续发力，不断激发创新活力。 二是完善科技金融，赋能创新"续航"。天津经开区天使投资基金正式发布，基金总规模5亿元，投资领 域重点聚焦人工智能生成技术等未来技术产业化新赛道，及经开区"4+1"优势产业未来化新方向。通过 投早、投小、投硬科技，为"含金""含绿"又"含新"的初创科技型企业注入资金活力，助力企业加速迈 过"最先一公里"。 三是推进平台搭建，汇聚科创"动能"。一季度天津经开区共有12家市级企业重点实验室获批，数量位列 全市第一。市级以上企业技术中心数量首次突破百个，区域企业的创新实力迈上新台阶。同时，天津科 技大学生物源纤维制造技术全国重点实验室成功获批，为区域产学研深度融合提供强大的支撑，有力推 动科技成 ...

Core Viewpoint - OpenAI has decided to maintain its non-profit oversight and control over its operations, transitioning its for-profit entity into a Public Benefit Corporation (PBC) to align with its mission while considering shareholder interests [1][2][5]. Group 1: Organizational Structure Changes - OpenAI's for-profit limited liability company (LLC) will transform into a Public Benefit Corporation (PBC), ensuring that the non-profit organization retains control and becomes the majority shareholder [2][3][5]. - The mission of OpenAI remains unchanged, focusing on ensuring that artificial general intelligence (AGI) benefits all of humanity [4][30]. - The previous restructuring plan aimed to reduce the non-profit's influence, but the revised plan strengthens the non-profit's control over the company's operations [5][30]. Group 2: External Pressures and Legal Challenges - OpenAI faced significant external pressure regarding its proposed transition to a for-profit model, with notable opposition from early investors like Elon Musk, who filed a lawsuit against the company [9][10]. - Various organizations, including former employees and labor groups, petitioned state attorneys general to prevent OpenAI from becoming a for-profit entity, citing concerns over the abandonment of its charitable mission [10][11]. Group 3: Financial Implications and Future Outlook - OpenAI's recent $40 billion funding round included conditions that could reduce the investment if the company does not fully transition to a for-profit entity by the end of 2025 [15]. - The company aims to evolve its structure to better serve its mission while ensuring that AI benefits a wide range of communities, with a focus on health, education, and public service [33][34].

机器之心报道 最近，OpenAI 向营利性公司的倾斜遭到了很多人的诟病，尤其是马斯克。他曾于 2024 年 2 月在加州法院 起诉 OpenAI 及 CEO 山姆・奥特曼，指控其背离了公司成立时造福人类的非营利使命，转而追求利润最大 化。同时，马斯克特别提到了 OpenAI 与微软的密切合作，谴责其成为微软事实上的闭源子公司，违背了最 初的开源承诺。 在不断受到马斯克、前员工、AI 伦理学者和监管机构的多重压力下， OpenAI 于今日正式宣布放弃将公司 完全转为营利性机构的计划 。 简单来讲，OpenAI 澄清了关于其自身结构的四个事实，分别是： OpenAI 董事会主席 Bret Taylor 的完整公告内容如下： 编辑：杜伟 OpenAI 终于「妥协」了。 一是，OpenAI 最初是一家非营利组织，目前由该非营利组织监督和控制。未来，OpenAI 将继续由该非营 利组织监督和控制。 二是，自 2019 年起，OpenAI 营利性有限责任公司（LLC）一直隶属于非营利组织，并将转型为公益公司 （PBC）—— 一种以目标为导向的公司结构，必须兼顾股东利益和使命。 三是，非营利组织将控制 PBC，并成为 PB ...

Group 1 - The emergence of superintelligence is predicted to occur by 2026, which could represent a pivotal moment in human history, potentially leading to a choice between submission to or rebellion against this new intelligence [2][3] - The author, Zhu Jiaming, posits that artificial intelligence is creating a "third existence" that fundamentally alters human civilization, distinct from physical and spiritual existence [2][3] - The development of AI is marked by three waves: machine learning, deep learning, and the current trend of generative AI models, which have significantly advanced AI capabilities [4][5][6] Group 2 - The introduction of AI into economic activities challenges traditional economic theories, particularly the assumption of "rational agents" and the concept of resource scarcity [8][9] - AI's efficiency surpasses human labor, leading to a potential redefinition of economic principles such as division of labor and employment goals [9][10] - The integration of AI into various sectors is expected to create new academic disciplines, such as AI economics and AI sociology, to explore the interactions between AI and societal structures [10] Group 3 - Concerns about AI's potential to surpass human control have sparked discussions about the need for humanity to undergo a transformation towards a new human identity [11][12] - The concept of transhumanism suggests that humans can transcend their natural limitations through technology, with advancements in bioengineering supporting this vision [12] - The relationship between human intelligence and AI is seen as one of mutual understanding, with AI playing a crucial role in future wealth creation [12]

Group 1: Core Insights - Artificial intelligence (AI) is a strategic technology driving a new wave of technological revolution and industrial transformation, recognized for its strong "leading goose" effect and as a key engine for cultivating new productive forces [1][2] - The Chinese government has prioritized AI development, with "AI+" included in the 2024 Government Work Report and a focus on fostering future industries through AI actions [1][2] - The AI industry is experiencing rapid growth, entering a high-speed development phase driven by technological innovation and commercial application [1][4] Group 2: Historical Development - The development of AI has gone through several phases, from initial exploration in the 1950s to significant breakthroughs in the 21st century, particularly with the rise of deep learning and applications in various fields [2][3] - The emergence of large-scale pre-trained models in 2020 marked a new stage in AI development, enabled by advancements in high-performance computing and the availability of massive datasets [4][5] Group 3: Industry Structure - The AI industry is divided into core industries, which include software algorithms, hardware products, and platform services, and fusion application industries that integrate AI into traditional sectors [6][9] - The core AI industry and fusion application industry mutually promote each other, leading to a relatively complete industrial system with continuous technological innovation and expanding investment [6][9] Group 4: Global Landscape - The United States leads in AI development, focusing on high-performance general models before penetrating vertical industries, creating a "top-down" development path [7][8] - In contrast, the European Union and Japan leverage their resource endowments and industrial foundations, with the EU focusing on data resources and regulatory frameworks, while Japan emphasizes AI integration with manufacturing [8][9] Group 5: China's AI Development - China's AI development emphasizes overall layout and industrial synergy, leveraging its comprehensive manufacturing base to prioritize application and industry collaboration [9][10] - From 2017 to 2024, China's core AI industry scale surged from 18 billion to 600 billion yuan, with over 4,700 companies and leading global positions in research output [10][11] Group 6: Application Scenarios - AI large models are primarily applied in voice assistants and intelligent customer service, with the intelligent customer service market reaching 3.94 billion yuan in 2023 [12][13] - In manufacturing, AI models enhance efficiency across the entire production chain, with applications in design, predictive maintenance, and quality inspection [14][15] Group 7: Challenges and Opportunities - The AI industry faces challenges in core technology breakthroughs and ecosystem construction, with a need for improved collaboration among large and small enterprises [16][17] - Despite significant investments, the AI sector in China is still developing, with a need for sustainable business models and effective integration of AI into various industries [18][19] Group 8: Future Directions - Future AI development in China will focus on top-level design, tackling key core technologies, and enhancing the industrial ecosystem to foster innovation and application [19][20] - Collaborative innovation in AI applications will be essential, with a focus on addressing common challenges across industries and promoting the integration of AI into various sectors [21]

Core Insights - The rise of AI large models, particularly with the advent of ChatGPT, has sparked discussions about the potential of general artificial intelligence leading to a fourth industrial revolution, especially in the financial sector [1][2] - The narrative suggesting that the Western system, led by the US, will create a technological gap over China through its "algorithm + data + computing power" advantages is being challenged as more people recognize the potential and limitations of AI [1][2] Group 1: Historical Context and Development - The concept of artificial intelligence dates back to 1950 with Alan Turing's "Turing Test," establishing a theoretical foundation for AI [2] - The widespread public engagement with AI is marked by the release of ChatGPT in November 2022, indicating a significant shift in AI's development trajectory [2] Group 2: Current State of AI in Industry - The arrival of large models signifies a new phase in AI development, where traditional machine learning and deep learning techniques can work in tandem to empower manufacturing [4] - AI applications in the industrial sector are transitioning from isolated breakthroughs to system integration, aiming for deeper integration with various industrial systems [5] Group 3: AI's Impact on Manufacturing - AI can enhance productivity, efficiency, and resource allocation in the industrial sector, serving as a crucial engine for economic development [5] - The current landscape in China features a coexistence of large and small models, with small models primarily handling structured data and precise predictions, while large models excel in processing complex unstructured data [5][6] Group 4: Challenges in AI Implementation - AI's application in manufacturing is still in its early stages, with significant reliance on smaller models for specific tasks, while large models are yet to be fully integrated into production processes [8][9] - The industrial sector faces challenges such as high fragmentation of data, lack of standardized solutions, and the need for highly customized AI applications, which complicates the deployment of AI technologies [10][11] Group 5: Future Directions and Strategies - The goal is to achieve a collaborative system of large and small models, avoiding a singular focus on either, to explore the boundaries of AI capabilities and steadily advance application deployment [20][21] - A phased approach is recommended for AI integration in industry, starting with traditional small models in high-precision environments and gradually introducing large models in less critical applications [19][24] - The development of a robust evaluation system tailored to industrial applications is essential for assessing the performance of AI models in real-world settings [19][26]

近年来，人形机器人的关注度明显增加了：主要是因为这个领域渐有爆发之势。黄仁勋在去年的不止一 个场合都说过机器人即将迎来"ChatGPT时刻"，NVIDIA眼中在生成式AI之外的下一个爆点就是机器 人。 行业内像ROSCon这样的机器人开发者大会越来越火；宇树科技机器人亮相春晚扭秧歌,特斯拉擎天柱机 器人叠衣服等引发极大关注......这些都让人感觉机器人正在以前所未有的速度发展。然而，4月初的一场 人形机器人马拉松比赛却为火热的机器人浇下来一盆冷水。 01 人形机器人水平低于市场预期 最近，北京亦庄半程马拉松暨人形机器人半程马拉松在南海子公园南门开跑。这是全球首个人形机器人 半程马拉松，20支人形机器人赛队与跑步爱好者一起冲出起跑线，在21.0975公里长的赛道上挑战极 限。 然而，多个网传视频显示，此前被寄予厚望的宇树科技G1人形机器人在跑步过程中摔倒，此后站起继 续比赛。 宇树科技对此发布声明称："宇树最近完全没有参与任何比赛，主要忙着准备人形机器人的格斗直播。 G1人形机器人，从去年发货开始，已经出售给全球非常多的客户，使用了很久。所以这次马拉松比 赛，也有好几个独立的团队使用我们的机器人。比如我们的客 ...

如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容来自 Source：编译自麦肯锡，谢谢。 半导体行业的经济利润增长（即该行业公司产生的收益超过其资本成本的比例）一直强劲。相对于平均经济利润率排 名前30的其他行业，半导体行业取得了显著进步，从2000-2004年的第15位跃升至2016-2020年的第4位 ，再到2020-24 年的第3位。 麦肯锡分析显示，半导体行业在其第一个十年创造了相对温和的经济利润，约为380亿美元，其中大部分来自英特 尔。除英特尔外，其他行业参与者的经济利润微乎其微甚至为负。在第二个十年，这个日趋成熟的行业经历了显著整 合，并扩大了代工模式；智能手机等新技术的出现，为计算机以外的领域开辟了新的增长途径。因此，盈利能力大幅 提升，2010年至2019年期间，该行业创造了4500亿美元的经济利润。 2020 年至 2024 年间，该行业创造的总经济利润价值为 4,730 亿美元，超过了之前整个十年创造的利润（图 1）。 2020 年至 2024 年，半导体行业创造的经济利润将超过前十年 经济利润的飙升主要得益于人工智能的爆炸式增长，以及半导体在汽车、工业等市场的新应用。此外，疫情导致 ...