黄仁勋的这次演讲，质量有点高。 10月6日，他出现在纽约，美国城堡证券（Citadel Securities）举办的一场闭门对话，对话在10天后，也 就是昨天（10月16日），被公布。 台下坐着华尔街最敏锐的一群人，掌控着全球数万亿美金的资金流；台上，黄仁勋穿着那件标志性的黑 皮夹克，讲了一个横跨30年的故事。 从显卡、到加速计算、再到AI工厂，他几乎重述了整部"人工智能的演化史"。 这场对话密度，像在听一位哲学家回顾工业革命，只不过他谈是算力。最让我印象深的，是他那句几乎 带点预言意味的话： The future of computation is 100% generated.未来的计算，将是百分之百的生成式。 听完，我觉得，他像在讲人类的下一种生产方式。现在，请允许我把理解后的内容汇报给你。 先说说他都说了什么吧。 回到了1993年，那个互联网还没普及的年代。 那时所有投资都在押CPU，因为摩尔定律还在，晶体管越做越小，性能就能翻倍。所有人都在追"更通 用、更强大的处理器"。 但他看到了极限，他说： 通用技术的最大问题，是它往往对"极难的问题，没那么好用"。 所以，他干了一件"反主流"的事，造一个专门为 ...

听完，我觉得，他像在讲人类的下一种生产方式。现在，请允许我，把理解后的内容，汇报给你。 黄仁勋这次演讲，质量有点高。 10月6日，他出现在纽约，美国城堡证券（Citadel Securities）举办的一场闭门对话，对话在10天后，也 就是昨天，被公布。 台下坐着华尔街最敏锐的一群人，掌控着全球数万亿美金的资金流；台上，黄仁勋穿着那件标志性的黑 皮夹克，讲了一个横跨30年的故事。 从显卡、到加速计算、再到AI工厂，他几乎重述了整部「人工智能的演化史」。 这场对话密度，像在听一位哲学家回顾工业革命，只不过他谈是算力。最让我印象深的，是他那句几乎 带点预言意味的话： The future of computation is 100% generated.；未来的计算，将是百分之百的生成式。 01 先说说他都说了什么吧；回到了1993年，那个互联网还没普及的年代。 那时所有投资都在押CPU，因为摩尔定律还在，晶体管越做越小，性能就能翻倍。所有人都在追「更通 用、更强大的处理器」。 但他看到的了极限，他说： 通用技术的最大问题，是它往往对「极难的问题'，没那么好用」。 所以，他干了一件「反主流」的事，造一个专门为「难 ...

人工智能能够不断自我迭代，通过持续学习提升自身能力，产生新的效果，这是一个重要的变化。AI不再是辅助工具，而是可独立完成任务的"数字劳动 力"，企业估值从"工程公司"转向"效益共创"。未来AI企业的价值判断标准关键指标应该是是否能从"提供工具"升级为"输出数字劳动力"，直接创造可量化 的效益。 北京商报讯（记者 和岳）10月17日，HICOOL 2025全球创业者峰会举办"AI赋能数字经济高质量发展论坛"，亿欧董事长、中国产业发展促进会产业集群副 秘书长王彬在谈及人工智能商业落地时表示，行业应该关注两个方面，一个是让"车"开得更快、更稳，另一个则是开辟新的赛道，因此人工智能提效是新的 增长点，方向则是依靠硬科技和前沿技术，推动未来产业发展，从而进一步提升实体产业的作用。 | the control concession in the control of the controlled | | | | --- | --- | --- | | | the control control of the consideration | | | | 1. We a | | | One of Concession | | ...

Core Insights - The conversation between Huang Renxun and Sequoia Capital highlights NVIDIA's evolution from a 3D graphics chip startup to a cornerstone of global AI computing [1][3] - Huang emphasizes the need to invent both technology and market simultaneously, stating that the future of AI will reshape industries worth trillions of dollars [4][10] Group 1: Founding NVIDIA - NVIDIA was founded in 1993, driven by the insight that general-purpose technology struggles with complex problems, leading to the need for accelerated computing [4][18] - The company faced a "chicken or egg" dilemma, needing a large market that did not exist at the time, which led to the creation of the modern 3D graphics video game market as a "killer application" for its technology [5][24] Group 2: Birth of CUDA - The introduction of the CUDA platform marked a pivotal shift from a hardware company to an ecosystem platform, allowing scientists to leverage GPU power for various complex problems [7][28] - CUDA served as a bridge for researchers to utilize GPU capabilities, alleviating computational bottlenecks caused by the slowing of Moore's Law [7][28] Group 3: AI Revolution - The launch of AlexNet in 2012, which achieved significant breakthroughs in computer vision using NVIDIA GPUs, marked a turning point for the company, leading to a full commitment to deep learning [8][29] - NVIDIA's development of the DGX-1, the first supercomputer designed for AI, solidified its role as a core infrastructure builder in the AI revolution [8][33] Group 4: AI Factory Concept - Huang describes the future data center as an "AI factory," where the value is measured by the computational throughput per unit of energy, transforming how infrastructure is perceived [9][37] - This new paradigm explains why major companies invest heavily in NVIDIA's infrastructure, as it serves as a direct revenue engine rather than a cost center [9][37] Group 5: Future Waves of AI - The next wave of AI will involve "digital labor" (agent AI) and "physical AI" (robotics), which will reshape industries worth trillions [10][41] - Huang envisions a future where human and digital workers coexist, enhancing productivity across various sectors [10][41] Group 6: Paradigm Shift to Generative Computing - Huang predicts a fundamental shift from "retrieval-based" to "generative" computing, where information is generated in real-time rather than retrieved [11][41] - This transformation will redefine human-computer interaction, moving towards collaborative creation rather than simple command execution [11][41] Group 7: AI Investment and Opportunities - Huang notes that AI is not just about new companies but is transforming existing large-scale enterprises, with significant revenue implications [39][40] - The emergence of AI-native companies and the shift towards AI-driven operations in major firms represent a new market opportunity worth trillions [40][41] Group 8: Robotics and Physical AI - Huang discusses the potential of robotics, suggesting that if AI can generate actions in a virtual environment, it can also control physical robots [50][51] - The future of robotics will involve multi-modal AI that can operate across various physical forms, enhancing capabilities in numerous applications [55][56]

Core Insights - The core insight of the article revolves around NVIDIA's strategic evolution from a graphics processing company to a leader in AI infrastructure, emphasizing the importance of "accelerated computing" and the development of AI factories to support the next wave of technological growth. Group 1: NVIDIA's Strategic Vision - NVIDIA recognized the limitations of general-purpose computing and the end of Moore's Law, leading to the adoption of an "accelerated computing" strategy since its inception in 1993 [1][17] - The company introduced CUDA to promote GPU utilization in scientific research, significantly impacting deep learning advancements [1][22] - NVIDIA's collaboration with leading researchers in AI, such as Geoffrey Hinton and Andrew Ng, facilitated breakthroughs in competitions like ImageNet, solidifying its position in the AI revolution [1][23] Group 2: AI Factory and Technological Advancements - The launch of the DGX-1 AI factory in 2016 marked NVIDIA's entry into large-scale computing, achieving approximately a 10x performance leap across generations [2][26] - NVIDIA's "full-stack collaborative design" approach integrates hardware and software, enabling significant performance improvements while reducing costs for clients [2][33] - The company predicts that AI will create two trillion-dollar markets: digital labor (Agentic AI) and physical AI (robotics) [3][4] Group 3: Market Impact and ROI - AI has already demonstrated substantial ROI in hyperscale data centers, with NVIDIA asserting that AI-driven systems have generated hundreds of billions in returns [3][36] - The shift from traditional CPU-based systems to AI-driven deep learning represents a multi-hundred billion dollar transformation in the industry [36] - Companies like Meta have successfully leveraged NVIDIA's technology to recover significant market value, showcasing the tangible benefits of AI investments [39][40] Group 4: Future Opportunities - The future of computing is expected to be 100% generative, with AI factories serving as essential infrastructure for real-time content generation [5][64] - The emergence of digital labor and physical AI is anticipated to significantly enhance productivity across various sectors, representing a substantial portion of the global economy [38][56] - NVIDIA's advancements in AI and robotics are set to revolutionize industries, with the potential for AI to operate in various physical forms, such as autonomous vehicles and humanoid robots [50][55]