这场对话的双方，一位是特斯拉前AI总监、OpenAI创始成员 Andrej Karpathy，他是「Software 2.0」概念的提出者，一直站在编程范式转移的最前沿； 另一位是 Claude Code 的缔造者、Anthropic 的核心人物 Boris Cherny，他正在亲手打造终结传统编程的工具。 他们的讨论不仅仅是关于工具的迭代，更像是一场关于人类技能边界的哲学思辨。 当代码不再由人类一个个字符敲击而出，我们究竟是在进化，还是在退化？ 这场对话揭示了一个残酷而兴奋的事实： 我们正处于从「命令式编程」向「声明式意图」彻底转型的奇点。 Andrej Karpathy与Claude Code负责人Boris Cherny展开了一场关于编程未来的终极对谈。面对AI接管100%代码编写的现状，Karpathy坦言人类正处于 「脑萎缩」与能力进化的十字路口。 本文深度解析了从Software 2.0到Agentic Coding的范式转移，揭示了在Opus 4.5等强力模型加持下，程序员如何从「搬砖工」进化为「指挥官」，以及不 仅要面对效率的飞跃，更要警惕「垃圾代码末日」的隐忧。 2026年的开篇，科技圈被一 ...

Core Insights - The article discusses the evolution of software paradigms from Software 1.0 (traditional coding) to Software 2.0 (neural network weights) and now to Software 3.0 (prompts), emphasizing the significance of natural language as a programming language [3][8][11] - It highlights the emergence of Large Language Models (LLMs) as a new type of operating system (LLM OS), reshaping the computing ecosystem and enabling new forms of interaction with AI [5][8] - The article identifies the greatest opportunity in developing "partially autonomous" AI applications, which enhance human capabilities rather than aiming for full automation [10][11] Software Paradigms - Software 1.0 involves traditional coding with specific programming languages, while Software 2.0 utilizes neural networks where data sets are prepared to optimize parameters [3] - Software 3.0 introduces prompts as the programming language, allowing for a more accessible and intuitive way to interact with AI [3][8] LLM as an Operating System - LLMs are compared to a new operating system, where they act as the CPU, with their expanding context window serving as memory, and external tools functioning as peripherals [5][8] - The current state of LLMs is likened to the 1960s computing era, where they are primarily cloud-based and accessed through thin clients [6][8] Opportunities in AI Development - The article emphasizes the need to understand the "mental model" of LLMs, which exhibit human-like characteristics but also have limitations such as hallucinations and memory issues [7][10] - Successful AI applications should focus on creating a feedback loop where AI-generated content is quickly verified by humans, enhancing efficiency [10] Accessibility of Software Development - Software 3.0 lowers the barrier to entry for programming, allowing individuals without formal training to create software through natural language [11] - The future of software design must cater not only to humans but also to intelligent agents, necessitating new standards and tools for better interaction [11][12]

Summary of NVIDIA Corporation (NVDA) Conference Call Company Overview - **Company**: NVIDIA Corporation (NASDAQ: NVDA) - **Event**: Morgan Stanley Technology, Media & Telecom Conference - **Date**: March 5, 2025 - **Key Participants**: Colette Kress (EVP & CFO), Joseph Moore (Morgan Stanley) Key Points Financial Performance - **Q4 Earnings**: - EPS of $0.89, beating expectations by $0.04 [8] - Revenue of $39.33 billion, representing a 77.94% year-over-year increase, beating expectations by $1.19 billion [8] Demand and Product Insights - **Data Center Growth**: - 18% sequential growth in data center revenue, primarily driven by the Hopper architecture [8][10] - Strong demand for Hopper products despite delays in the Blackwell architecture [12][14] - **Post-Training Compute Demand**: - Post-training and model conditioning require significantly more compute power than pre-training, indicating a shift in market focus [16][19] - Reasoning models are becoming increasingly complex, driving additional compute needs [20][22] Product Development and Supply Chain - **Blackwell Architecture**: - Achieved $11 billion in revenue for Blackwell in Q4, exceeding initial expectations [31] - Focus on ensuring customer needs are met and scaling supply to match demand [34][36] - **Networking Business**: - Opportunities for growth in both InfiniBand and Ethernet, with a focus on AI applications [52][54] - Significant improvements in networking performance, with plans for continued growth [56] Competitive Landscape - **Custom Silicon**: - Custom silicon discussions have been ongoing for several years, but NVIDIA maintains a strong market position with a 90% share [40][42] - The complexity of designing chips and ensuring compatibility remains a challenge for competitors [41][44] Export Controls and Regulatory Environment - **AI Diffusion Rules**: - Ongoing discussions with the U.S. government regarding the implications of AI diffusion rules set to take effect in May [63][65] - NVIDIA is advocating for a more efficient licensing process to facilitate global compute distribution [66][68] Additional Insights - **Future Outlook**: - Anticipation of continued strong demand for Blackwell and a focus on scaling supply to meet this demand [58][61] - Emphasis on the importance of reasoning models and their impact on future compute requirements [19][22] This summary encapsulates the key insights and developments discussed during the conference call, highlighting NVIDIA's strong financial performance, product demand, competitive positioning, and regulatory considerations.