📄 中文摘要
在讨论人工智能偏见时，通常关注的是有害输出或不公平预测。然而，模型在理解句子之前，首先会将其拆分为标记，这一过程在无形中影响了用户的体验。标记化过程决定了用户支付的费用、获得的上下文量以及模型推理的能力。对于使用不太常见语言的用户来说，可能会面临更高的费用和更差的性能。标记化并非中立，它对不同语言的支持程度不一，导致了碎片化的现象。
人工智能不仅仅是有偏见的。它是碎片化的——而你正在为此付出代价。
出处: AI Isn’t Just Biased. It’s Fragmented — And You’re Paying for It.
发布: 2026年2月19日
📄 English Summary
AI Isn’t Just Biased. It’s Fragmented — And You’re Paying for It.
When discussing AI bias, the focus is often on harmful outputs or unfair predictions. However, before a model comprehends a sentence, it first breaks it into tokens, a process that subtly influences the user experience. Tokenization determines how much users pay, the amount of context they receive, and the model's reasoning capabilities. Users of less common languages may face higher costs and poorer performance. Tokenization is not neutral, leading to fragmentation in support for different languages.