It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.
另一个被反复忽视的约束，来自抽佣本身的结构变化。早期的抽佣往往表现为清晰的单一比例，但随着平台业务复杂化，收费逐渐被拆分为技术服务费、营销推广费、会员费、广告费等多项组合。抽佣不再是一个价格，而是一套规则。对供给侧而言，理解与比较成本显著上升；对平台而言，收费的可解释性开始影响交易秩序与信任基础。
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