我觉得我出现了大模型倦怠
I think I have LLM burnout

原始链接: https://www.alecscollon.com/blog/llm-burnout/

截至 2026 年 7 月,作者的日常工作流程已因大语言模型(LLM)而发生根本性转变,这些模型现已成为其编程、研究和项目设计流程的核心。尽管这些工具无疑提高了生产力,并使作者能够探索陌生的技术领域,但对人工智能的依赖也滋生了日益增长的职业倦怠感。 除了应对模型幻觉和代码错误所带来的预期挫折外,作者发现自己越来越反感人工智能生成文本在风格上的单调性。那些僵硬的表达模式——表现为断续的措辞、重复的结构以及刻意使用的表情符号——已成为令人心烦意乱的源头。 尽管作者承认人类也会犯错,但人工智能输出那种独特且刻板的统一性正在造成一种倦怠感。尽管大语言模型十分实用,但作者却难以在提高效率的同时,克服对“AI 审美”日益加深的恐惧,这使得他们在面对既高效又令人心力交瘁的工作流程时,只能选择“咬牙坚持”。

Hacker News 关于“大模型(LLM)倦怠”的讨论凸显了开发者中日益增长的共识:在人工智能辅助工作流的推动下,维持持续生产力的压力正导致疲惫和幻灭。 该讨论帖中的核心主题包括: * **“瓶颈”效应:** 开发者对于人工智能催生的大量任务感到筋疲力尽,这使他们从创造性的问题解决者变成了“机器”管理者。编程的本质已从技艺转变为重复性的质量保证(QA)和对人工智能输出的“盖章”确认。 * **信任与维护:** 压力的一大来源是验证人工智能生成代码的难度。一些人建议将严格的测试驱动开发(TDD)和静态类型作为必要的保障措施,但也有人对人工智能生成代码的质量和可维护性持怀疑态度。 * **满足感的流失:** 一些开发者开始质疑自己的职业道路,指出他们对解决复杂技术问题的热情,正被围绕人工智能的怪癖、表情符号和陈词滥调进行“提示词工程”这种琐碎任务所取代。 * **应对机制:** 建议的缓解措施包括:使用严格的风格指南来“人性化”输出内容;将人工智能定位为交流伙伴而非主要编码员;以及刻意放慢节奏,以避免陷入“时刻在线”的陷阱。
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原文

I use LLMs a lot. By current dev standards, my usage rate is probably average, and my methods are probably primitive. I work on one task at a time and discuss it with Claude Code (at work) or Codex (at home, for now). Sometimes, I let the assistant write code, but I read the output thoroughly, understand it, and revise it. I’m not in the deep end of autonomous agents or agent orchestration. Still, I spend hours each day interacting with LLMs across work and home. That’s a hell of a lot more than I did a few years ago, and I probably don’t go a day without reading AI-generated text.

My job has changed from designing and writing code to designing code, describing the design to an LLM, reviewing code the LLM produces, and then finally writing code. The LLM steps expose me to approaches I might not have considered or been aware of. I also feel more comfortable in areas where I don’t have deep knowledge.

My main project right now is to establish a framework for large-scale, unsupervised code generation in our codebase. When I’m not working with Claude to create tooling, I’m sifting through the unsupervised agent’s (Qwen’s) output. Either way, I’m reading LLM content.

If I want to know something, I’ll probably ask ChatGPT or read Gemini’s overview unless I know what sites I want to check. I still have to fall back to browsing when the LLM’s answer is wrong, but it’s good enough for many casual queries, especially when useless AI-generated articles clutter the search results.

It’s been this way for about a year, and I don’t see myself stopping. I feel more productive with LLMs, and I think continually learning how to use them effectively is valuable. However, my disposition has changed a bit in the last few months. Some small part of me has started to dread reading LLM output because I know what I’m going to find. False assumptions and hallucinations. Emphatic, staccato fragments. ✨ Excessive emojis 🚀. It’s not just me—these are real patterns (🤮).

On their own, none of these annoyances gets to me. Together, though, they’ve gotten me sick of LLM writing in a hurry.

I’m not trying to condemn LLMs. Humans are fallible, too—we can be just as unreliable or annoying. The problem is repetition. LLMs write in the same style, and they make the same kinds of mistakes. Dealing with the same thing over and over is wearing me out. I can use personalization features if the interface offers them, but some idiosyncrasies seep through. And of course, I don’t control the style of content generated by other people.

I don’t know how to deal with this feeling yet. I didn’t expect to be so bothered by it. Frustration at a flaky tool is understandable, but the writing patterns grind my gears, too. For now, I’ll grit my teeth and hope I don’t lose my lunch.

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