OpenAI或正在向垂直整合的"AI帝国"转型。Altman透露公司正进行"激进的基础设施押注"，其规模之大需要整个行业参 与。近期与英伟达、甲骨文、AMD等巨头合作后，他预告更多交易将公布，旨在撬动整个AI产业链。Altman强调AI发展 与能源紧密关联，AI竞赛或已是算力、资本和能源的全方位较量。 这一战略直接解释了OpenAI近期与英伟达、甲骨文、AMD等科技巨头达成的一系列合作。Altman预告， 未来数月将有更多此类合作公布，显示其正试图撬动"从电子到模型分发"的整个产业链。 这或也意味着AI竞赛正从算法转向一场关乎 算力、资本和能源 的全方位斗争。 硬·AI Altman同时将AI的未来与能源的未来直接挂钩，指出AI的指数级增长将需要更廉价、更丰富的能源。他预 测，长期的解决方案将是太阳能加储能与先进核能的结合，并断言核能的成本将是决定其能否快速普及、 进而支撑AI发展的关键变量。 Altman谈到公司愿景时表示，OpenAI不仅仅是研究实验室， 更是集消费者AI订阅服务、超大规模基础设 施运营和前沿AI研究于一体的综合体，致力于构建通用人工智能（AGI）并使其对人类有益。 作者 | 龙 玥 编辑 ...

OpenAI正在从一家研究实验室向一个垂直整合的"AI帝国"转型。 10月8日，OpenAI首席执行官Sam Altman在与知名风投公司a16z联合创始人Ben Horowitz的一场最新对话中透露，OpenAI已决定进行"非常激进的 基础设施押注"，其规模之大需要整个行业参与。 他解释说，这一决策基于对未来一到两年内模型能力的强大信心，因为他们预见到即将到来的模型将创造巨大的经济价值，而当前的扩张速度已 无法满足未来的需求。 这一战略直接解释了OpenAI近期与英伟达、甲骨文、AMD等科技巨头达成的一系列合作。Altman预告，未来数月将有更多此类合作公布，显示 其正试图撬动"从电子到模型分发"的整个产业链。 这或也意味着AI竞赛正从算法转向一场关乎算力、资本和能源的全方位斗争。 Altman同时将AI的未来与能源的未来直接挂钩，指出AI的指数级增长将需要更廉价、更丰富的能源。他预测，长期的解决方案将是太阳能加储能 与先进核能的结合，并断言核能的成本将是决定其能否快速普及、进而支撑AI发展的关键变量。 Altman谈到公司愿景时表示，OpenAI不仅仅是研究实验室，更是集消费者AI订阅服务、超大规模基础设 ...

Core Insights - The emergence of AI scientists represents a significant advancement in scientific research, enabling faster hypothesis generation and experimental design, as demonstrated by the recent validation of a new bacterial gene transmission mechanism by Google's AI in just 48 hours [1][2] Group 1: AI Scientist Development - AI scientists are not physical robots but intelligent agents powered by large language models, capable of generating scientific hypotheses and research plans autonomously [1] - The global competition among research institutions to develop AI scientist systems is intensifying, with two main categories: AI as research assistants and fully autonomous scientific discovery systems [2][3] Group 2: Research Assistant Systems - The first category focuses on creating AI systems that assist human scientists, providing interdisciplinary knowledge and research ideas, exemplified by Stanford University's "Virtual Laboratory" which successfully designed 92 antiviral nanobodies [2] Group 3: Autonomous Discovery Systems - The second category aims to develop fully autonomous systems capable of scientific discovery, with examples including Japan's "Fish AI" which produced a computer science paper and the "Future Home" AI system that discovered a drug for dry macular degeneration [3] Group 4: China's AI Scientist Initiatives - China is accelerating the development of AI scientist systems, with initiatives like the "Virtual Scientist" system and the "Feng Deng Gene Scientist" system, which has identified previously unreported gene functions in staple crops [4] Group 5: Future Prospects - The future may see more physical AI scientists assisting in complex research environments, such as "AI crop geneticists" and "AI soil scientists," transforming previously fictional scenarios into reality [5]

Core Viewpoint - The article discusses the emergence of SciMaster, an AI scientific assistant developed by Shanghai Jiao Tong University, DeepMind Technology, and Shanghai Algorithm Innovation Institute, which is claimed to be the world's first truly general-purpose scientific AI agent [5][10]. Group 1: Introduction to SciMaster - SciMaster has gained significant attention in the research community, with its invitation codes being sold for nearly a thousand yuan, indicating high demand [5]. - It integrates advanced capabilities such as literature search, theoretical calculations, experimental design, paper writing, and collaboration, significantly enhancing research efficiency [7][11]. Group 2: Macro Trends in AI - The AI field is transitioning from data and computing power reliance to practical applications, as noted by mathematician Terence Tao [9]. - The concept of an "AI scientist" is at the forefront of this trend, with SciMaster filling a gap in the availability of practical AI research assistants [10]. Group 3: Functional Capabilities of SciMaster - SciMaster covers the entire research process, including reading, calculating, conducting experiments, and writing reports [11]. - It utilizes a vast database of 170 million research documents to provide reliable information and can trace every assertion back to its source [11][14]. - The system can perform calculations and execute experiments through integration with automated laboratory systems [14][15]. Group 4: Performance and Testing - SciMaster has demonstrated its capabilities by achieving a new state-of-the-art score of 32.1% on the Humanity's Last Exam benchmark, surpassing competitors like OpenAI and Google [28]. - The assistant can handle general queries and conduct deep research, providing comprehensive reports based on extensive data collection and analysis [30][31]. Group 5: Future Prospects - The development of SciMaster represents a significant step towards a new era of collaborative scientific exploration between humans and AI [16][49]. - The company aims to expand SciMaster's capabilities to cover a broader range of scientific knowledge, indicating a commitment to advancing AI in research [50].