这话，出自大神 卡帕西 。 是的，这位AI Coding界的明星人物，开始公开吐槽一件事——代码已经不是AI编程的瓶颈了，部署才是。 梦瑶 发自 凹非寺 量子位 | 公众号 QbitAI 我是真发现了，搁现在写代码不是难事儿，难的是你得搞一堆部署服务！！ 更关键的是，人家卡帕西还直接把问题点破了： 所有应用产品的开发流程，都应该变成可以被代码直接调用的东西，最好人类完全不用手动配置！ 卡帕西这番感慨一出，帖子底下的开发者网友们更是憋了一肚子牢骚，纷纷吐槽起AI编程里的各种部署坑： AI编程部署的困扰，显然已经成了程序员们公认的大难题。 卡帕西：应用开发这事儿，部署忒难！ 主要吧，卡帕西还是被自己过往的一段应用开发经历折磨坏了…… 这事儿还要说回去年他自己用AI搓的一个「菜单图片生成器」产品—— MenuGen 。 当时做这个产品的动机也很简单，主要是卡帕西平时去餐馆吃饭，看到那种纯文字菜单，经常不知道这道菜到底长啥样。 本来是想美美干饭，但是把时间全花在谷歌搜索菜单名这事儿上了，气不打一出来。 （于是人家干脆直接自己用AI搓了一个～） 你别说，从下面这产品效果看还真不错，把菜单输入进去就能呈现一个带食物图片的 ...

Core Viewpoint - The future of AI requires a "ten-year patience" and a focus on developing "Iron Man suit" style enhancement tools rather than fully autonomous robots [3][30][34]. Group 1: Software Evolution - The software industry is undergoing a fundamental transformation, moving from Software 1.0 (human-written code) to Software 2.0 (neural networks) and now to Software 3.0 (using natural language as a programming interface) [6][10][11]. - Software 1.0 is characterized by traditional programming, while Software 2.0 relies on neural networks trained on datasets, and Software 3.0 allows interaction through prompts in natural language [8][10][11]. Group 2: LLM as a New Operating System - Large Language Models (LLMs) can be viewed as a new operating system, with LLMs acting as the "CPU" for reasoning and context windows serving as "memory" [12][15]. - The development of LLMs requires significant capital investment, similar to building power plants and grids, and they are expected to provide services through APIs [12][13]. Group 3: LLM's Capabilities and Limitations - LLMs possess encyclopedic knowledge and memory but also exhibit cognitive flaws such as hallucinations, jagged intelligence, anterograde amnesia, and vulnerability to security threats [16][20]. - The dual nature of LLMs necessitates careful design of workflows to leverage their strengths while mitigating their weaknesses [20]. Group 4: Partial Autonomy Applications - The development of partial autonomy applications is a key opportunity, allowing for efficient human-AI collaboration [21][23]. - Successful applications like Cursor and Perplexity demonstrate the importance of context management, multi-model orchestration, and user-friendly interfaces [21][22]. Group 5: Vibe Coding and Deployment Challenges - LLMs democratize programming through natural language, but the real challenge lies in deploying functional applications due to existing infrastructure designed for human interaction [24][25]. - The bottleneck has shifted from coding to deployment, highlighting the need for redesigning digital infrastructure to accommodate AI agents [25][26]. Group 6: Infrastructure for AI Agents - The digital world is currently designed for human users and traditional programs, neglecting the needs of AI agents [27][28]. - Proposed solutions include creating direct communication channels, rewriting documentation for AI compatibility, and developing tools that translate human-centric information for AI consumption [28][29]. Group 7: Realistic Outlook on AI Development - The journey towards AI advancement is a long-term endeavor requiring patience and a focus on enhancing tools rather than rushing towards full autonomy [30][31]. - The analogy of the "Iron Man suit" illustrates the spectrum of autonomy, emphasizing the importance of developing reliable enhancement tools in the current phase [33][34].