英伟达的护城河

澎湃新闻01-01

前几天，一个炒股的朋友跑来问我：“英伟达是不是不行了？像是四面楚歌了。谷歌有TPU，OpenAI有Triton，华为有升腾，AMD也在后面追着喊‘兄弟，等等我’。”我说你先别急着替英伟达写悼词。你这问题，暴露了一个常见毛病：很多人把“护城河”理解成知识产权、渠道、品牌、规模、网络效应、用户认知——这些都对，但在硬科技领域，它们往往只是“表面那层漆”。真正能挡人的，是一种不体面、没有浪漫气息、写出来...

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href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260101185821a6e89f10&s=b><strong>澎湃新闻</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>前几天，一个炒股的朋友跑来问我：“英伟达是不是不行了？像是四面楚歌了。谷歌有TPU，OpenAI有Triton，华为有升腾，AMD也在后面追着喊‘兄弟，等等我’。”我说你先别急着替英伟达写悼词。你这问题，暴露了一个常见毛病：很多人把“护城河”理解成知识产权、渠道、品牌、规模、网络效应、用户认知——这些都对，但在硬科技领域，它们往往只是“表面那层漆”。真正能挡人的，是一种不体面、没有浪漫气息、写出来...</p>\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260101185821a6e89f10&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"LU2931357623.SGD","symbol_name":"MANULIFE GF GLOBAL SEMICONDUCTOR OPPORTUNITIES \"AA\" (SGDHDG) 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moat","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVD3.UK":0.6,"NVDX":0.6,"3NVD.UK":0.6,"NVDD":0.6,"NVD2.UK":0.6,"NVDS.UK":0.6,"NVD":0.6,"NVDS":0.6,"NVDA":1.97,"2NVD.UK":0.6,"NVDU":0.6,"SNVD.UK":0.6,"NVIW.SI":0.6,"NVDY":0.6},"content_text":"前几天，一个炒股的朋友跑来问我：“英伟达是不是不行了？像是四面楚歌了。谷歌有TPU，OpenAI有Triton，华为有升腾，AMD也在后面追着喊‘兄弟，等等我’。”我说你先别急着替英伟达写悼词。你这问题，暴露了一个常见毛病：很多人把“护城河”理解成知识产权、渠道、品牌、规模、网络效应、用户认知——这些都对，但在硬科技领域，它们往往只是“表面那层漆”。真正能挡人的，是一种不体面、没有浪漫气息、写出来甚至有点像在给加班洗白的东西：工程积淀。工程积淀是什么？它不是某个天才的灵光一现，更不是一纸专利。它更像是：你在一条路上走了二十年，沿途遇到几十万个坑。每个坑都不大，但都很贵、很烦、很脏。你填完一个坑，前面又冒出一个坑。你一边填坑一边骂街，一边还得保证车继续跑。最后这条路就成了高速公路——别人也想走，但他们要先把二十年的坑补齐。这类东西有两个特点：数量巨大，无法预测。所以它很难被“钞能力”瞬间复制。钱能买人，能买设备，能买流量，但买不到“曾经踩过的坑”。这就像健身：你可以买蛋白粉，买私教，买器械，但你买不到别人已经流过的汗。你甚至可以买到别人的汗——也没用。我们可以通俗地叫它“微创新”，但是它有两个特点：数量巨大，无法预测。这意味着这类创新，几乎只能通过实践和时间长出来。英伟达如此，ASML、ARM也是如此。就像ARM一样，指令集理论大家都知道，但是，要性能，要稳定，要低功耗，只有ARM做得到。英伟达的护城河里，最硬的两块石头，大概是：CUDA和NVLink（以及它背后的整套集群网络能力）。先说CUDA。CUDA最大的优势不是“先进”，而是“习惯”。习惯这东西很要命。你要让全世界工程师换生态，就像你要让全世界改用一种新筷子——它也许更符合人体工程学，但大家已经用旧筷子夹了二十年花生米。CUDA经过二十年积累，形成了一套从编译器、驱动、库、算子到框架适配的“工程宇宙”。你以为你在买显卡，其实你在买一个东西：跑起来的确定性。工程师最怕的不是难，而是“难还没意义”。他愿意为模型效果熬夜，但不愿意为了装驱动熬夜。你让他调参，他眼睛发光；你让他配环境，他就会思考人生是不是该出家。你随便找个开源项目，很多都是PyTorch+CUDA。用英伟达，通常是：gitclone，pipinstall，回车，然后你就可以去泡咖啡——有时候连咖啡都不用泡，报错会帮你醒脑。不用英伟达呢？你会获得一个“深度参与工程史诗”的机会：第一天：装驱动。第二天：编译依赖。第三天：某个算子没实现。第四天：你写了个替代版本，速度慢得像在用算盘训练大模型。第五天：老板问你“为什么还没结果”，你说“我在和宇宙进行兼容性谈判”。这时候你会明白：英伟达卖的不是卡，是不折腾的权利。再说NVLink和网络那一套。很多人看GPU只看单卡算力，这就像评价一个军队只看单兵格斗。训练大模型不是单挑，是群殴，而且是那种“上万个人要同时听懂同一句口令”的群殴。单卡再猛，如果集群通信像村口小路堵车，你照样跑不动。英伟达靠NVLink，再加上Mellanox那套网络交换能力，把大量GPU变成一个能协同工作的整体。它不是简单把显卡堆在一起，而是给它们装了神经系统。AMD这些年单卡确实追得很凶，这很好。但大规模集群这事，像写一本长篇小说：你不能只写好开头就宣布自己赢了。《红楼梦》最难的从来不是开篇，是你写到八十回还能不崩。那么，对手们有没有机会？当然有。世界又不是只有一个主角，主角也会掉头发。谷歌TPU很强，但它的强有点像“自家厨房的神锅”——特别适合谷歌的菜系。你在外面想买一口回来做饭，往往没那么顺。它更多是体系内优势，不是“广泛可得、随手可用”的大众生态。OpenAI的Triton很有野心，想做一种“万能翻译官”，让你写一次代码，到处都能跑。听起来很美，像世界语。问题是：理想世界里大家讲世界语，现实世界里大家讲英语——因为英语已经形成了偷懒的规模优势。工程师的信仰不是“公平”，是“省事”。他写代码是为了跑模型，不是为了参与语言学实验。AMD的策略是“便宜大碗”。这也对，甚至很可能越来越对。但它现在经常给人的感觉是：你买到的是一台发动机很猛的车，变速箱、维修体系、加油站网络还在建设中。你问销售：“这车怎么样？”他告诉你：“马力真大。”你再问：“那我能从新加坡开到吉隆坡吗？”他沉默了一下，回答说：“理论上可以。”这就是工程积淀的残酷：有些路必须一步一步走。你可以加快，但不能跳过。就像生孩子——哪怕你是世界首富，雇最好的医生，甚至雇十个孕妇，你也没法在一个月里把孩子生出来。时间这东西很讨厌，但它同时也是最可靠的护城河：它不接受辩论，只接受发生过。有人说，AI芯片终将商品化。拉长时间看，大概率是对的。但还有个更大的事实经常被忽略：训练侧被挤压，反而会把推理侧抬起来；而推理侧的需求很可能远超训练侧。工业制造、具身智能、电动汽车、军事防务、家庭智能中心……这些不是“互联网应用”，它们更像把现实世界一寸一寸搬进计算里。你可以把它叫“物理世界自动化”。这块蛋糕足够大，大到你很难只用“训练芯片会不会商品化”来判断输赢。所以，我更愿意这么收束：英伟达的护城河，表层是CUDA、NVLink、生态惯性；深层是它二十年“填坑”的复利——而黄仁勋的厉害之处在于，他一直知道自己在填的不是某个坑，而是一条路。你要短期打败英伟达，不只是做出一张更快的卡。你得做出一种新的人类：不怕装驱动，不怕编译，不怕报错，最好还喜欢写算子。如果真有这种人类出现，英伟达才算遇到天敌。但我怀疑，人类进化没那么快。（作者鲁漫为产品经理，科技行业工作者）","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":2,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 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