漂移原则：为什么系统即使在“正常运作”时也会变得更糟
我注意到一个在社交平台、新闻周期、用户体验设计以及现在的人工智能助手中普遍存在的模式。在某个时刻，系统开始完全按照其设计的目的运行，但用户体验却变得更糟。漂移原则是我试图描述这一时刻的概念：
当一个系统的优化速度超过了它保持上下文的能力时，现实的真实性就开始滑落。
这样的例子比比皆是。社交媒体的内容流围绕着驱动参与度的内容收紧，内容范围缩小，直到一切都感觉像是对自身的重新混合。客户支持流程变得更友好，但帮助性却降低，因为指标更看重语气而非解决方案。智能助手变得更加流畅和谨慎，同时悄然降低了具体性。即使是个体用户也会陷入这种模式，一旦你学会了什么有效，你的在线行为就会慢慢趋向于最安全的自我版本。
没有发生灾难性的事情。系统仍然在运作。但让它感觉真实的质感逐渐变薄。你获得了效率，却缺乏深度；获得了音量，却缺乏多样性。系统优化的目标与人类实际体验之间的差距就是漂移现象出现的地方。
我很好奇这里的其他人如何看待这种模式，或者你们是否在不同领域中见过更好的描述方式。
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I keep noticing a pattern across social platforms, news cycles, UX design, and now AI assistants. At some point the system starts doing exactly what it was built to do, but the experience gets worse. The Drift Principle is my attempt to describe that moment:<p>When a system optimizes faster than it can preserve context, fidelity to reality starts to slip.<p>Examples are everywhere. Social feeds tighten around whatever drives engagement, and the range of content shrinks until everything feels like a remix of itself. Customer support flows become friendlier but less helpful because the metric rewards tone over resolution. Helpful AI assistants become smoother and more cautious while quietly dropping specificity. Even individual users fall into it, once you learn what works, your online behavior slowly converges toward the safest version of yourself.<p>Nothing catastrophic happens. The system still functions. But the texture that made it feel real gradually thins out. You get efficiency without depth, volume without variety. That gap between what the system optimizes for and what humans actually experience is where the drift shows up.<p>Curious how others here think about this pattern, or whether you’ve seen better language for describing it across domains.