AI is making me dumb
552 points
• 4 days ago
• Article
Link
使用人工智能写作、编程或起草文件的诱惑越来越难以抵挡。然而，这种便利代价高昂，依赖这些工具似乎正在侵蚀人们创作原创内容的能力。即便是过去不认为自己写得差的人，频繁使用人工智能也会明显削弱个人技能。
这种衰退往往源于由自我怀疑和冒名顶替综合症驱动的恶性循环。 AI 生成的内容常缺乏个人特色，不像作者本人的语气，也难以传达预期的细微差别。这种脱节使人更难独立创作，因为产出听起来像机器写的，而非真实的自我表达。
在软件开发领域，这种影响更为深刻。完全依赖提示（prompting）近两年、几乎一行代码都没亲自写过后，很多人开始感觉自己正在丧失最基本的编程技能。职业本身不会消失，总会需要会读写代码的人，但向以人工智能为主导的开发转变，确实让许多人失去了曾经定义他们职业生涯的技艺与自豪感。
不过也有希望，人工智能或许能扭转行业长期以来的一些问题。几十年来，软件开发者的需求远超供给，这在一定程度上拉低了行业的专业标准。在计算机科学成为主流职业之前，编程主要由学术界、物理学家和数学家主导。 AI 有可能提高真正掌握这门技艺所需的门槛，从而把一些曾经丧失的职业精神带回行业。
甚至把这番挣扎写出来，也是与想借助这些工具的冲动作斗争。总有挥之不去的焦虑，担心文字表述不清或读起来别扭，于是忍不住把内容交给像 Claude 这样的 AI 去验证。要摆脱对这些工具的依赖，就必须积极对抗它们助长的自我怀疑，夺回自己的声音与能力。
The temptation to use AI for writing, coding, or drafting documents is becoming increasingly difficult to resist. However, this convenience comes at a steep cost, as the reliance on these tools seems to be actively diminishing the ability to produce original work. Even for someone who didn't consider themselves a poor writer before, the frequent use of AI is causing a noticeable decline in personal skill and capability.
This decline often stems from a cycle involving self-doubt and imposter syndrome. When AI generates content, the results often lack a personal touch, sounding nothing like the individual's own voice or failing to convey the exact nuances intended. This disconnect can make the user feel even less capable of producing work independently, as the output feels artificial rather than authentic.
In the realm of software development, the impact has been even more profound. After relying entirely on prompting for nearly two years without writing a single line of code, there is a growing sense of loss regarding the fundamental skill of programming. While the profession itself isn't disappearing, and there will always be a need for people who can actually read and write code, the shift toward AI-driven development is causing many to lose the very craft that once defined their lives.
There is a hope, however, that AI might actually help reverse a long-term trend in the industry. For decades, the massive demand for software developers has outstripped the supply, which has arguably led to a decline in the professional standards of the field. Before computer science became a mainstream profession, programming was primarily the domain of academics, physicists, and mathematicians. By potentially raising the bar for what is required to truly master the craft, AI might help return some of that lost professionalism to the industry.
Even the act of writing about this struggle is a battle against the urge to use these tools. There is a constant, nagging anxiety that a piece of writing might not make sense or might read awkwardly, which leads to the impulse to run text through an AI like Claude for validation. Overcoming this dependency requires actively fighting back against the self-doubt that these technologies feed on, in order to reclaim one's own voice and skills.
302 comments • Comments Link
- 有经验的开发者在使用人工智能编写代码时常感到持续的不安，因为他们必须不断审查并补充每一份输出，以确保其正确性与可维护性。
- 大型语言模型往往倾向于增加代码量而非精简实现，随着代码库通过冗长或重复的实现不断膨胀，技术债务可能不断累积，接近极高水平。
- "逐词预测"这一架构特性使得模型容易冗长啰嗦，经常不知道何时停止，也难以在不丢失上下文的情况下进行高层次的项目规划。
- 在循环中使用 AI 代理会加剧问题：一个代理可能会通过增加更多代码来回应另一个代理的审查，而不是回溯并寻找更好的架构方案。
- 关于维护成本上升是否会使高冗余和"附带复杂性"的代价降低存在争论，这可能使人们偏离追求极简且高度优化代码的方向。
- 有效的人工智能工作流程通常把模型当作处理例行事务的实习生或助手，例如生成模板代码、编写单元测试或进行重构，同时把核心架构设计和创新逻辑留给人类来完成。
- 过度依赖人工智能可能导致认知退化，开发者会丧失解决问题的"肌肉记忆"、语法熟练度以及对所构建系统的深刻理解。
- 初级开发者面临最大风险：如果在学习阶段缺乏必要的挣扎和实践经验，他们可能无法建立起将来监督 AI 工具所需的基础技能。
- 对一些人来说，人工智能是强大的能力放大器，能把枯燥重复的任务卸载出去，使他们能处理更复杂的工程问题，但这需要严格的自律以避免凭感觉写代码。
- AI 驱动的开发正在改变开发者的角色：从逻辑的创造者转为需求的协调者与产出验证者，更强调系统设计而非语法细节。
讨论集中在人工智能带来的巨大生产力提升与对工程手艺及个人专业素养的长期风险之间的矛盾。虽然把重复性任务交给 AI 使很多人能够专注于更高层次的架构设计并从中获益，但普遍共识是：缺乏严格人类监督的凭感觉编码会导致臃肿且不可维护的代码库，并引发认知能力的下降。人们尤其担忧行业的未来：如果初级开发者跳过了手动编程中必要的磨炼，他们如何才能培养起深厚的技术直觉。归根结底，最有效的做法似乎是有纪律的协调运用——把 AI 当作扩展能力的工具，而不是替代批判性思维的全部手段。 • Experienced developers often feel a persistent sense of unease when using AI to write code, driven by a need to constantly review and supplement every output to ensure correctness and maintainability.
• LLMs tend to prioritize adding more code rather than simplifying it, which can lead to an asymptotic approach toward 100% technical debt as the codebase grows through verbose or redundant implementations.
• The architectural nature of "next-token prediction" makes models prone to verbosity, often struggling to know when to stop or how to structure projects at a high level without losing context.
• Using AI agents in loops can exacerbate problems, as one agent may attempt to address another's review by adding even more code rather than backtracking to find a better architectural approach.
• There is a debate regarding whether the rising economics of maintenance makes high verbosity and "accidental complexity" less costly, potentially shifting the focus away from minimal, highly optimized code.
• Effective AI workflows involve treating the model as an intern or an assistant for rote tasks, such as boilerplate, unit tests, or refactoring, while reserving core architectural and novel logic for human implementation.
• Relying too heavily on AI can lead to cognitive atrophy, where developers lose the "muscle memory" of problem-solving, syntax, and the ability to deeply understand the systems they are building.
• Junior developers face the greatest risk, as the lack of struggle and hands-on experience during the learning phase may prevent them from building the foundational skills required to eventually oversee AI tools.
• For some, AI acts as a powerful force multiplier that allows them to tackle much more complex engineering problems by offloading the mundane, though it requires strict discipline to avoid "vibe coding."
• The shift toward AI-driven development changes the developer's role from a creator of logic to an orchestrator of requirements and a verifier of outputs, emphasizing systems design over syntax.
The discussion centers on the tension between the massive productivity gains offered by AI and the long-term risks to engineering craft and individual expertise. While many find value in offloading repetitive tasks to focus on higher-level architecture, there is a strong consensus that "vibe coding" without rigorous human oversight leads to bloated, unmaintainable codebases and cognitive decline. A significant concern exists for the future of the profession, specifically regarding how junior developers will develop deep technical intuition if they bypass the essential struggles of manual programming. Ultimately, the most successful approach appears to be one of disciplined orchestration, where AI is used as a tool for expansion rather than a total replacement for critical thinking.