Where Are the Vibecoded Photoshops?
本文的核心论点是，"vibecoding"——即指责 AI 工具让没有技能的人轻松产出复杂软件——是个神话。作者指出，尽管 AI 已经普及两年，但并未出现所谓的"vibecoded"等价物——像 Photoshop 、 Excel 或 Maya 这类复杂、架构性的软件仍然没有。这一缺失表明，AI 并没有真正降低构建非平凡、连贯系统的门槛。作者认为，对 vibecoding 的指控本身就是一种"slop"，即未经证实的断言，用来在没有证据的情况下否定他人的工作。
作者把软件开发分成三个层面：Level 1 是机械性地敲代码和处理语法；Level 2 涉及验证、测试与质量保证；Level 3 则是架构判断，决定要做什么、如何确保系统在现实中能站得住脚。 AI 虽然大幅降低了 Level 1 的成本和劳力，但对 Level 2 和 Level 3 几乎没有影响，而真正的门槛就在后两者。作者认为，那些指责别人 vibecoding 的人，往往是过度认同 Level 1 工作的人——当这一层被自动化时，他们会感到受威胁。
文章暗示，vibecoding 的指控是一种防御性反应：当人们看到 AI 辅助的作品，就会下意识地认为那很容易，是靠 AI 做出来的，然后把这种直觉当成结论发布出来。这样的指控之所以流行，是因为它"听起来对"，而不是因为它真的对。更具讽刺意味的是，指责他人产出未经验证的成果，本身就是一种未经验证的断言——指责者正在做的，正是他们指责 vibecoder 做的：提出没有定义、没有检验、不可证伪的说法。
作者以个人经验作证。像 SoulPlayer——一个带有严格 90 项测试验证框架的 C64 音乐播放器——以及一系列耗费数月、需要定制工具链的 AI 音乐视频，都说明严肃的 AI 辅助工作离不开深入的 Level 2 和 Level 3 工作。作者有 demoscene 背景，常被请去解决"别的办法都不行"的技术难题，这证明他们知道真正的门槛在哪里。尽管有资格去否定他人的成果，作者仍拒绝使用"vibecoded"这类指控。
拒绝这种指责既是道德上的，也是策略性的。作者意识到，这样的指控会消耗被指责者的时间和士气，迫使他们为自己辩护而不是继续创作。作者一生中多次遭遇排斥性指责——作为神经多样者、残障者、自由职业者和 demoscener——因此理解这种手法的形态，不愿予以复制。围绕 AI 使用的羞耻经济靠恐惧维系，而非基于真正可耻的行为；作者拒绝为其添柴。文章最后向指责者发出挑战：拿出证据来，展示那些据称会威胁职业的 vibecoded Photoshop 或其他复杂产物。
The central argument of this piece is that "vibecoding," the accusation that AI tools allow unskilled people to produce complex software effortlessly, is a myth. The author points out that despite two years of widespread access to AI, there are no "vibecoded" equivalents of complex, architectural software like Photoshop, Excel, or Maya. The absence of these artifacts suggests that AI has not actually lowered the barrier to creating non-trivial, coherent systems. The author argues that the accusation of vibecoding is itself a form of "slop," an unverified claim made to dismiss others' work without evidence.
The author breaks down software development into three levels. Level 1 is the mechanical act of typing code and syntax. Level 2 involves verification, testing, and quality assurance. Level 3 is architectural judgment, deciding what to build and how to ensure it holds together in the real world. While AI has significantly reduced the cost and effort of Level 1, it has not impacted Levels 2 or 3, where the actual "gate" of meaningful software creation resides. The author contends that those who accuse others of vibecoding are often those who over-identified with Level 1 work, and feel threatened when that layer is automated.
The piece suggests that the "vibecoding" accusation is a defense mechanism. When people see AI-assisted work, they assume it was easy because it was made with AI, and they post that feeling as if it were a finding. This accusation travels because it feels right, not because it is right. The author notes a bitter irony, the accusation that someone produced unverified output is itself being produced as unverified output. The accuser is doing the very thing they accuse vibecoders of, making claims without definition, testing, or falsification.
The author draws from personal experience to illustrate these points. Projects like SoulPlayer, a C64 music player with a rigorous ninety-test verification harness, and a series of AI music videos that required months of custom toolchain development, demonstrate that serious AI-assisted work involves deep Levels 2 and 3 engagement. The author has a demoscene background and has been the person called in when nothing else works, proving they know where the gate is. Despite having the credentials to dismiss others' work, the author refuses to use the "vibecoded" accusation.
The refusal to use the accusation is both ethical and strategic. The author recognizes that the accusation costs the target time and morale, forcing them to defend themselves instead of building. Having been on the receiving end of exclusionary accusations throughout life, neurodivergent, disabled, freelancer, demoscener, the author understands the shape of the move and will not replicate it. The shame economy around AI use runs on fear, not on actual shameful behavior, and the author refuses to feed it. The piece ends with a challenge to accusers to produce the evidence for their claims, the vibecoded Photoshops and other complex artifacts that supposedly threaten the profession.
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讨论围绕一个核心观点：以 AI 是否能像 Photoshop 那样产出复杂、完整的单体软件来衡量其局限，是不恰当的。许多参与者认为，AI 的真正影响在于让个人能够为特定任务构建小型、个性化的工具，而不是复制整个专业套件。对话强调了从通用软件向定制解决方案的转变：AI 降低了非技术用户构建功能性、单一用途应用的门槛，但人们也对这些所谓的"氛围编码"项目的质量、可维护性和可扩展性持怀疑态度，担心技术债务、测试不足和长期可行性问题。讨论还涉及既有软件的经济与文化惯性、编码与更高层次设计的差别，以及 AI 通过增量式、专业化工具而非直接克隆来颠覆市场的潜力。
要点包括：
• AI 并没有取代像 Photoshop 这样的单体应用，而是让个人能够为特定任务快速搭建小而个性化的工具，从而绕开对全功能软件套件的依赖。
• "氛围编码的 Photoshop 在哪里？"这一问法被许多人视为失之偏颇，因为 AI 更重要的价值在于赋能非开发者构建利基问题的定制化解决方案。
• 非开发者利用 AI 创建功能性、单一用途应用的例子随处可见，例如用于管理葡萄酒数据库或医学院题库的工具——这些都是高度个性化的，并非面向大众市场。
• 批评者指出，氛围编码式的应用常缺乏稳健性、可维护性和合理架构，可能因技术债务和测试不足而难以长期存续。
• 即便借助 AI，复刻像 Photoshop 这样的大型成熟应用仍成本高昂且复杂，代码规模、历史遗留功能和生态系统集成都是重大障碍。
• 有人把 AI 比作 3D 打印：适合利基、小批量的生产，但不能替代工业级的软件开发。
• 一些人认为 AI 正在降低软件创造门槛，类似智能手机让摄影更普及，但产出质量往往较低，难以与专业工具相抗衡。
• 讨论呈现出两派观点：一派认为 AI 是软件创造的民主化力量，另一派则认为它更多产出低质量的"垃圾"，冲击专业标准。
• 大家也意识到 AI 仍在快速演进，尽管眼下可能还无法"氛围编码"出 Photoshop 级别的应用，但能力在迅速提升。
• 已有软件所依赖的经济与文化惯性——用户习惯、文件格式兼容性和生态锁定——即便在 AI 辅助下，也使直接竞争变得困难。
讨论揭示了对 AI 在软件开发中作用的根本分歧：一方面，人们乐观地认为 AI 会催生新一波个性化、用户自建的工具，绕开传统软件巨头；另一方面，怀疑者关心这些工具的质量与可持续性，更认为 AI 应当成为增强专业开发者的工具，而非替代复杂的软件生态系统。总体共识是：尽管 AI 还未能像 Photoshop 那样生成成熟的单体应用，它已经改变了个人和小团队通过代码解决问题的方式，但在质量和生命周期方面仍存在重大隐忧。 The discussion centers on the claim that AI has not yet produced complex, monolithic software replacements like Photoshop, and uses this as a benchmark for AI's limitations. Many participants argue that this is a flawed metric, as the real impact of AI is enabling individuals to create small, personalized tools for specific tasks rather than replicating entire professional suites. The conversation highlights a shift from generalized software to bespoke solutions, with AI lowering the barrier for non-technical users to build functional, single-purpose applications. However, there is skepticism about the quality, maintainability, and scalability of such "vibe-coded" projects, with concerns about technical debt, testing, and long-term viability. The debate also touches on the economic and cultural inertia of established software, the distinction between coding and higher-level design, and the potential for AI to disrupt markets through incremental, specialized tools rather than direct clones.
• AI is not replacing monolithic applications like Photoshop but enabling individuals to create small, personalized tools for specific tasks, bypassing the need for full-featured software suites.
• The "Where are the vibecoded Photoshops?" question is seen by many as a flawed benchmark, as AI's real value lies in empowering non-technical users to build bespoke solutions for niche problems.
• Examples abound of non-developers using AI to create functional, single-purpose apps, such as a wine database manager or a medical school question bank, which are highly personalized and not intended for mass market.
• Critics argue that vibe-coded apps often lack robustness, maintainability, and proper architecture, and may not survive long-term due to technical debt and lack of testing.
• The cost and complexity of replicating a massive, mature application like Photoshop are still prohibitive, even with AI, due to the sheer scale of code, legacy features, and ecosystem integration.
• AI is compared to 3D printers: useful for niche, low-volume production but not a replacement for industrial-scale software development.
• Some argue that AI is lowering the barrier to entry for software creation, similar to how smartphones made photography accessible, but the output is often lower quality and not competitive with professional tools.
• The discussion highlights a tension between those who see AI as a democratizing force for software creation and those who view it as producing low-quality "slop" that undermines professional standards.
• There is a recognition that AI is still evolving, and while it may not yet be able to vibe-code a Photoshop-class application, its capabilities are rapidly improving.
• The economic and cultural inertia of established software, including user habits, file format compatibility, and ecosystem lock-in, makes direct competition difficult even with AI assistance.
The discussion reveals a fundamental divide in how AI's impact on software development is perceived. On one side, there is optimism about AI enabling a new wave of personalized, user-created tools that bypass traditional software giants. On the other, there is skepticism about the quality and sustainability of such tools, and a belief that AI's real value lies in augmenting professional developers rather than replacing complex software ecosystems. The consensus seems to be that while AI is not yet capable of producing monolithic applications like Photoshop, it is already transforming how individuals and small teams approach problem-solving through code, albeit with significant caveats about quality and longevity.