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但斌看空英伟达了

易简财经03-04

昨天但斌的一条朋友圈掀起了轩然大波，长期看好纳斯达克的他居然开始看空英伟达了。他表示：“英伟达的周期，不是源自它‘2B’或者‘非平台’的属性，而是在于它始终是个‘资本品’/‘耐用消费品’。如果一个公司的产品是重复消费的‘快速消耗品’，需求量会相对稳定，像食品饮料、在线游戏、煤炭。但如果一个公司的产品是‘资本品’/‘耐用消费品’，因为可复用,下游需求量通常会呈现巨大波动性。所以，杰文斯悖论应用在算力...

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Nvidia","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDD":0.6,"NVDS":0.6,"NVDA":1,"SNVD.UK":0.6,"NVD2.UK":0.6,"3NVD.UK":0.6,"NVDY":0.6,"NVD3.UK":0.6,"NVIW.SI":0.6,"2NVD.UK":0.6,"NVDU":0.6,"NVDS.UK":0.6},"content_text":"昨天但斌的一条朋友圈掀起了轩然大波，长期看好纳斯达克的他居然开始看空英伟达了。他表示：“英伟达的周期，不是源自它‘2B’或者‘非平台’的属性，而是在于它始终是个‘资本品’/‘耐用消费品’。如果一个公司的产品是重复消费的‘快速消耗品’，需求量会相对稳定，像食品饮料、在线游戏、煤炭。但如果一个公司的产品是‘资本品’/‘耐用消费品’，因为可复用,下游需求量通常会呈现巨大波动性。所以，杰文斯悖论应用在算力上，有个错误我们之前没注意：模型成本下降，算力效率提升，AI场景确实会得到扩展，算力总需求也会因此大幅提升。但不一样的是，煤炭是一次性消耗资源，而算力是可重复使用的资源；即便每年算力需求总量大幅上升了，但产生算力的ai芯片需求量，也有可能是下降的！简单来说，因为‘可复用资本品’的属性，英伟达产品收入是AI需求的‘二阶导’，而收入增速是‘三阶导’。所以，英伟达周期波动性肯定非常大，即便在AI算力需求一直增长的情况下。”对此，DeepSeek认为，但斌的观点属于“偷换概念式正确”，存在明显的逻辑漏洞：1. 混淆资源属性：煤炭烧完就没了，但算力芯片是固定资产，这点没错。但芯片也有使用寿命（3-5年），且AI应用爆发带来的算力饥渴会迫使企业持续购置新芯片来扩容，就像LED灯省电了，但家家都装100盏灯，总耗电量反而飙升——这时候你会因为旧灯泡没坏就停止买新灯泡吗？2. 忽视技术迭代：AI模型复杂度每年翻倍（参考摩尔定律），现在够用的芯片三年后可能连基础推理都跑不动。就像10年前的显卡跑不了《原神》，效率提升抵不过需求膨胀。3. 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