黄仁勋罕见发布长文：定义AI“五层架构”，预判将创造更多就业（附全文）

凤凰网科技03-10

凤凰网科技讯 3月10日，英伟达CEO黄仁勋于周二发表了一篇罕见的关于人工智能的长篇博客文章，指出当前的AI基础设施建设仍处于极早期阶段。他强调，尽管目前行业已经投入了数千亿美元，但未来仍需要数万亿美元的持续投资来完善数据中心和相关底层设施。这是他自2016年以来发表的第七篇公开长文，阐述了对AI发展速度、访问权限以及治理模式的看法。在文中，黄仁勋对AI与传统技术进行了界定，指出AI已经打破了传统...

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3月10日，英伟达CEO黄仁勋于周二发表了一篇罕见的关于人工智能的长篇博客文章，指出当前的AI基础设施建设仍处于极早期阶段。他强调，尽管目前行业已经投入了数千亿美元，但未来仍需要数万亿美元的持续投资来完善数据中心和相关底层设施。这是他自2016年以来发表的第七篇公开长文，阐述了对AI发展速度、访问权限以及治理模式的看法。在文中，黄仁勋对AI与传统技术进行了界定，指出AI已经打破了传统软件的运作模式。他认为，传统软件仅仅是提取人类预先编写的存储指令，而目前的AI系统则能够基于上下文进行实时推理并按需生成智能。为了厘清产业结构，他再次提及了其在年初达沃斯论坛上提出的“五层架构”模型，明确指出AI生态自下而上依次由能源、芯片、基础设施、模型和应用构成，任何成功的上层应用都必须完全依赖底层设施乃至发电厂的持续支撑。针对技术发展对劳动力市场的冲击，黄仁勋认为AI非但不会削减岗位，反而会创造大量新的就业机会，尤其是在基础设施和熟练技术工种领域。他的逻辑在于，当AI接管了企业的日常程序化任务后，生产力的提升将转化为服务能力的扩容，进而带动企业的实质性增长与扩张。他总结指出，当下大量的底层设施尚未破土，配套劳动力尚未完成培训，AI产业真正的红利期和大规模建设才刚刚开始。以下为黄仁勋博客全文：AI是一块五层蛋糕黄仁勋 (Jensen Huang)2026年3月10日AI是当今塑造世界的最强大力量之一。它不仅是一个聪明的应用程序或单一的模型；它更是如同电力和互联网一样至关重要的基础设施。AI运行在真实的硬件、真实的能源和真实的经济基础之上。它吸收原材料，并将其转化为规模化的智能。每家公司都将使用它。每个国家都将建设它。要理解为什么AI会以这种方式发展，我们需要从第一性原理出发，去看看计算领域到底发生了哪些根本性的变化。从预编软件到实时智能在计算历史的大部分时间里，软件都是预先编写好的。人类编写算法，计算机执行算法。数据必须被精心结构化，存储在表格中，并通过精确的查询进行检索。SQL之所以不可或缺，是因为它让那个世界的运转成为可能。AI打破了这一模式。我们第一次拥有了能够理解非结构化信息的计算机。它能看懂图像、阅读文本、聆听声音并理解意义。它能对上下文和意图进行推理。最重要的是，它能够实时生成智能。每一个响应都是全新生成的。每一个答案都取决于你提供的上下文。这不再是检索存储指令的软件，而是能够按需推理和生成智能的软件。正因为智能是实时生产出来的，其底层的整个计算架构栈都必须被重新发明。作为基础设施的AI当你从工业角度审视AI时，它呈现为一个五层架构。第一层：能源位于最底层的是能源。实时生成的智能需要实时产生的电力。生成的每一个Token（词元）都是电子移动、热量管理以及能源转化为计算能力的结果。在此之下没有任何抽象层。能源是AI基础设施的第一性原理，也是系统能产生多少智能的绝对约束条件。第二层：芯片在能源之上是芯片。这些处理器旨在以大规模、高效的方式将能源转化为计算能力。AI工作负载需要极其庞大的并行计算能力、高带宽内存以及快速的互连。芯片层的进步决定了AI的扩展速度，以及智能成本的下降程度。第三层：基础设施芯片之上是基础设施。这包括土地、电力输送、冷却系统、建筑施工、网络，以及将成千上万个处理器协同编排为一台机器的系统。这些系统就是“AI工厂”。它们的设计初衷不是为了存储信息，而是为了制造智能。第四层：模型基础设施之上是模型。AI模型可以理解多种类型的信息：语言、生物学、化学、物理学、金融、医学以及物理世界本身。语言模型仅仅是其中的一个类别。一些最具变革性的工作正发生在蛋白质AI、化学AI、物理模拟、机器人技术以及自主系统等领域。第五层：应用位于最顶层的是应用，这里也是创造经济价值的地方。药物发现平台、工业机器人、法律助手、自动驾驶汽车。一辆自动驾驶汽车就是具身于机器中的AI应用，而一个类人机器人则是具身于躯体中的AI应用。同样的底层架构，不同的应用输出。这就是“五层蛋糕”架构：能源 → 芯片 → 基础设施 → 模型 → 应用。每一个成功的应用都会向上拉动其下方的每一层，一直延伸到维持其运转的发电厂。我们的建设才刚刚开始。目前我们仅仅投入了数千亿美元，仍有价值数万亿美元的基础设施等待建设。在世界各地，我们看到芯片工厂、计算机组装厂和AI工厂正在以史无前例的规模拔地而起。这正在成为人类历史上最大规模的基础设施建设。支持这一建设所需的劳动力是极其庞大的。AI工厂需要电工、水管工、管道工、钢铁工人、网络技术人员、安装工和操作员。这些都是高技能、高薪酬的岗位，而且目前供不应求。你不需要拥有计算机科学的博士学位就能参与到这场变革中来。同时，AI正在推动整个知识经济的生产力提升。以放射科为例，AI现在可以辅助读取扫描影像，但对放射科医生的需求仍在持续增长。这并非悖论。放射科医生的核心职责是照顾患者，读取扫描影像只是这过程中的一项任务。当AI承担了更多常规工作时，放射科医生就可以把精力集中在临床判断、医患沟通和患者护理上。医院的生产力随之提高，他们能服务更多的患者，也就会雇佣更多的人员。生产力创造了服务容量，而容量创造了经济增长。过去一年发生了什么改变在过去的一年里，AI跨越了一个重要的门槛。模型变得足够优秀，能够在规模化应用中发挥实质作用。推理能力得到提升，幻觉大幅减少，基础事实的准确性（Grounding）显著改善。基于AI构建的应用程序首次开始产生真正的经济价值。在药物发现、物流、客户服务、软件开发和制造业等领域的应用，已经展现出强劲的产品市场契合度（Product-Market Fit）。这些应用正强力拉动着它们下方的每一层结构。开源模型在其中扮演了关键角色。世界上大多数模型都是免费的。研究人员、初创公司、大型企业乃至整个国家，都依赖开源模型来参与高级AI的研发。当开源模型达到前沿水平时，它们不仅改变了软件本身，更激活了整个架构栈的需求。DeepSeek-R1 就是最好的例证。通过让强大的推理模型被广泛可用，它加速了应用层的技术采用，并相应增加了其底层对训练、基础设施、芯片和能源的需求。这意味着什么当你将AI视为必不可少的基础设施时，其深远影响便清晰可见。AI始于Transformer大语言模型。但它远不止于此。它是一场工业转型，将重塑能源的生产和消费方式、工厂的建造方式、工作的组织方式以及经济的增长方式。之所以要建设AI工厂，是因为智能现在是实时生成的；之所以要重新设计芯片，是因为效率决定了智能扩展的速度；能源之所以成为核心，是因为它设定了智能生产的总量上限；应用之所以加速爆发，是因为底层的模型已经跨过了门槛，终于能够在规模化层面真正发挥效用。每一层都在相互强化。这就是为什么这场基础设施建设如此庞大，为什么它同时触及了这么多行业，也是为什么它不会局限于单一国家或单一领域。每家公司都将使用AI。每个国家都将建设它。我们仍处于早期阶段。许多基础设施尚未建成。大量劳动力尚未接受培训。许多机遇还未被充分挖掘。但方向已经非常清晰。AI正在成为现代世界的基础性基础设施。而我们现在所做的选择——我们建设的速度有多快、参与的范围有多广、部署的方式有多负责任——将最终塑造这个时代的未来。（作者/于雷）更多一手新闻，欢迎下载凤凰新闻客户端订阅凤凰网科技。想看深度报道，请微信搜索“凤凰网科技”。","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":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 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