The problem gets worse in pipelines. When you chain multiple transforms – say, parse, transform, then serialize – each TransformStream has its own internal readable and writable buffers. If implementers follow the spec strictly, data cascades through these buffers in a push-oriented fashion: the source pushes to transform A, which pushes to transform B, which pushes to transform C, each accumulating data in intermediate buffers before the final consumer has even started pulling. With three transforms, you can have six internal buffers filling up simultaneously.
其次，规模和可复制性完全不同。Altman 想强调「per query」的效率，但他忽略了：人类智能没法「复制部署」到数据中心里无限扩容。AI 的真正优势恰恰在于「训一次，用一辈子」，而人类是「训一次，用一辈子还得继续喂」。如果真要比「单位智能产出每焦耳能量」，AI 在规模化后确实可能碾压，但用「养孩子总成本」来类比，反而把这个优势给模糊掉了。
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