微软CTO Kevin Scott：AI最大的瓶颈不是算力，是人类还没学会怎么用它

至顶AI实验室02-13

2026年2月3日，Cisco在旧金山举办第二届AI Summit，汇集了来自微软、英伟达、OpenAI、英特尔等公司的技术领袖，围绕AI经济的基础设施、应用与未来展开对话。在"Systems & AI"环节，Cisco总裁兼首席产品官Jeetu Patel与微软首席技术官Kevin Scott进行了一场约22分钟的炉边对谈。Scott在微软已进入第十个年头。2017年加入后，他主导了微软AI战略...

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laws会起作用、系统会变得非常强大、它会像一个平台一样让人在上面构建各种东西，这些都是相对清楚的。问题只是速度——而速度比我预想的快。\"他紧接着抛出一个核心判断：目前AI平台的能力增长还远没有到边际递减的阶段。与此同时，一个巨大的\"能力过剩\"（capability overhang）正悬在行业头顶——\"模型的能力远远超出了人们实际使用它们的方式。\"Scott认为，目前最接近充分利用模型能力的领域是编程。\"软件开发现在是一场绝对的疯狂（absolute frenzy）。你见过的最顶尖的程序员，那些最资深的人，现在完全被进度的速度淹没了。\"每年15万美元的推理账单这场\"疯狂\"背后有一个具体的数字。Scott透露，微软内部最积极使用编程Agent的团队，每年仅推理成本就高达约15万美元。这些团队的瓶颈早已从代码生成转移到了注意力——他们需要管理Agent产出的全部复杂性。\"你可以用这些编程Agent生产大量的代码，但没人能保证那些代码是好的。\"Scott说。代码审查已经成为新的瓶颈。他提了一个在管理工程团队25年间始终无解的老问题：什么才算真正的工程生产力？AI让这个问题变得更加尖锐——\"人们真的需要确保自己不是在把'忙碌'和'进步'混为一谈。\"好消息是，这种能力正在催生新一代创业公司。\"现在有些创业公司拿到的融资，只有两年前的十分之一，但团队很小，干的活却疯狂地多，推进速度极快。\"Scott说。代价是：重点完全转移到了选择和品味上——你得真正理解自己的问题领域、你的客户、你要做什么。我们把计算机科学教育变成了职业培训对话转向软件开发角色的未来时，Scott没有顺着\"角色融合\"的方向走，而是把话题拉回到一个更根本的地方。\"这是Kevin的个人观点，不是微软的观点。\"他先做了个声明，然后语气明显变得更直接：\"我已经像个老头子一样抱怨好几年了——我们把计算机科学教育变成了职业培训（vocational education）。学生去读CS学位是为了学怎么当程序员，但我想让他们回去学怎么当计算机科学家。\"他列出了一串他认为真正重要的能力：能不能进行算法思维？能不能分解问题？能不能选对值得花时间的问题？理不理解自己的工作在科学和社会的大图景里处于什么位置？当机器给出的结果不对时，有没有能力和好奇心一层一层往下挖，去找到底哪里出了问题？\"软件工程的很多'职业'面向，在未来几年会变化到面目全非。\"Scott说。而上面那些能力，才是留下来的东西。今年是日本高中毕业生的历史峰值Patel让Scott描绘两个未来——一个乐观的，一个悲观的。Scott选择从一个听起来并不乐观的故事开始。他最近和一位管理日本顶级教育机构的朋友聊天，对方随口说了一句让他愣住的话：\"今年是日本高中毕业生的峰值年（peak high school graduation）。从今年开始，每年毕业的高中生只会越来越少，去上大学的人也越来越少，然后一路往下走。\"Scott把这个现象放到全球维度上：日本只是走得比较快，中国、韩国、西欧的多个国家都在同一条路上。美国如果扣除移民因素，未来几十年也会进入人口下降。他母亲所在的农村地区，人口老龄化的速度更快，影响更明显。\"你能做的工作的人变少了，需要社会照顾的老年人却在快速增加。要维持我们习惯的生活水平，生产力的本质必须改变。\"他说。而纵观人类历史，解决生产力问题的方式从来都是技术干预。\"所以我觉得乐观的版本是：谢天谢地AI在这个时候出现了（thank god AI has come along when it has）。它给了我们一个至少是部分的答案，去应对未来几十年前所未有的劳动力变局。\"绿色的大屁股羊驼悲观的版本呢？Scott没有选择末日叙事。\"你可以自己挑一个，现在有一整个调色盘。\"他笑着说。他真正担心的是另一种失败模式：AI被用于肤浅的消遣，而不是去解决那些紧迫的真问题。他用自己孩子举了个例子——\"我的孩子一半时间用AI做特别有野心的事，比如他们的生物医学工程项目，比我在他们那个年纪的时候厉害太多了。另一半时间，他们在用AI生成绿色的大屁股羊驼（green llamas with big butts），然后把别人的脸贴上去。\"全场大笑。Scott接着说：\"我的GPU烧着呢，就为了这个。\"笑声之后，他把话收了回来：\"我希望我们能抵抗诱惑，不要让整个AI叙事变成'这个周末又有什么轰动事件'，或者'一群有权势的人怎么互相扔飞镖'。让我们多想想社会真正需要从这项技术中获得什么。\"平台公司的基因和混乱的耐心Patel问Scott，外界对微软最大的误解是什么。Scott的回答指向一个根本性的定位问题：微软是一家平台公司。\"我们基本上不做任何事情，除非它是某种让别人能拿去、在上面再造东西的平台。这写在公司的DNA里，好的坏的都包括在内。\"他说。作为一家有50年平台经验的公司，微软对技术变革中的混乱有\"无限的耐心\"——不会等到理想条件出现才动手，会早早进场，犯一堆错，也做对一堆事，然后在现实世界里打磨。说到企业软件的本质，Patel插了一句行业老梗：\"企业软件最糟糕的事是什么？比没有客户更糟糕的是——只有一个客户。\"意思是你永远不能关掉任何东西，只能不断往上加。Scott同意，但他也说这是一种巨大的特权。\"如果你把Cisco和微软在全世界运行的所有东西都拿掉，一切都会停摆。你得珍惜这种复杂的、乱七八糟的义务。\"他把这和自己作为工程师的本能联系在一起：\"平台问题、给别人做工具让他们去创造东西——这甚至是我业余爱好里做的事。这就是我在微软待了这么久的原因。\"从OpenAI合作到推理需求的无底洞Scott说他最自豪的贡献，是帮助识别了AI从狭义专家系统向通用平台的转变时刻。\"20年前我们做机器学习，基本上就是一帮量化专家在做非常窄的问题，比如预测某个人在某个场景下会不会点一个广告。\"转折点在2018年前后，Google发表BERT论文的时候，这个判断被彻底确认。Patel在此特别指出，Scott是微软与OpenAI合作关系的最初架构师（initial architect）。\"没有那次合作，我觉得世界不会从后来的那些成果中受益。\"Scott对此谦虚了一下，但补充说他特别自豪的一点是：强大的AI能力被公开地放了出来，不是被某一家硅谷公司藏在自己的基础设施里，而是任何人都可以注册一个API密钥就开始在上面构建。\"这种对强大AI能力的民主化，无论我在其中扮演了多小的角色，都是我引以为傲的。\"至于基础设施紧缺还要持续多久，Scott坦言自己一直觉得即将缓解，但需求总是持续爆炸。\"我看到未来12个月即将上线的能力，实在无法想象需求会减少。\"他指向编程Agent的案例：目前只有极少数开发者拥有15万美元级别的投入和野心，但所有开发者都可以从中受益。把这个逻辑推广到其他领域，推理需求只会上升。在芯片策略上，Scott强调微软50年来的成功都依赖合作伙伴关系。\"我们有自己的芯片，芯片非常好，但我们同时有巨大的英伟达硬件集群、巨大的AMD硬件集群。我们有大量的芯片多样性。\"无论是客户需求还是工作负载趋势驱动，最终的逻辑很简单：哪个最有成本效益，就大规模部署哪个。技术从来没有变成工具以外的任何东西Patel的最后一个问题是：你最希望人类对待技术的方式有什么改变？Scott的回答简洁而坚定：\"我希望每个人都记住，技术没有变成工具以外的任何东西。作为工具，它在那里等着我们用它做有趣的事。不存在什么不可逆转的技术趋势线会把我们推向唯一的路径。一切都是关于选择——我们选择拿这个工具做什么，怎么排优先级。\"他接着说了一段在技术圈子里相当少见的话：\"我真的、真的、真的希望我们每天走进办公室的时候，能想一想自己即将做的事情，问自己一句：这是在服务我的同胞人类吗（is this serving my fellow human beings well or not）？如果我们都能这样做，日子结束的时候我们会处于一个非常好的状态。\"Patel提到，上次问Scott同一个问题时，他给出的答案是希望人们不要把一切看作零和博弈。Scott回应说，零和问题都是痛苦的——全是稀缺、约束、赢家和输家。\"我们真正想从技术中获得的，是把那些零和的东西变成非零和的。这实际上是做技术的理由。\"核心问答Q1: Kevin Scott认为当前AI发展中最大的矛盾是什么？能力过剩（capability overhang）。模型的能力远超人们的实际使用水平，但基础设施仍然供不应求。编程领域最接近充分利用，最积极的微软团队每年仅推理成本就达15万美元，而绝大多数开发者还没有这种投入水平。同时，AI能力的增长还远没有到边际递减阶段，这意味着供需缺口短期内不会缩小。Q2:为什么他认为AI不是可选项，而是必需品？全球人口结构正在发生前所未有的变化。日本2026年将迎来高中毕业生的历史峰值，之后逐年递减；中国、韩国、西欧多国走在同一条路上；美国如果扣除移民也面临人口下降。老龄化加速、劳动力萎缩，社会需要在更少的人手下维持甚至提升生产力和生活水平。历史上人类解决生产力问题的方式从来都是技术干预，AI是当前时点最关键的技术选项。Q3:他对计算机科学教育最核心的批评是什么？CS教育被异化为职业培训，学生学的是怎么当程序员，而不是怎么当计算机科学家。Scott认为，随着AI让代码编写的\"职业性\"工作发生根本性变化，真正重要的能力是：算法思维、问题分解、选择正确的问题、理解技术在社会中的位置，以及当机器给出错误结果时能够层层深入去诊断原因的能力和好奇心。","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":1,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 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