内容来源：https://www.technologyreview.com/2026/03/13/1134184/why-physical-ai-is-becoming-manufacturings-next-advantage/

内容总结：

微软与英伟达携手推动“实体AI”落地，制造业智能化进入新阶段

长期以来，制造业通过自动化追求效率、成本与运营稳定，取得了显著成效。然而，面对劳动力短缺、复杂性增加以及加速创新的压力，传统自动化已不足以应对新挑战。行业转型正进入新阶段，其核心不再是孤立的AI工具或单个机器人，而是能够在物理世界中可靠运行的智能系统——“实体AI”。

“实体AI”指能感知、推理并作用于真实环境的智能。它标志着制造业从狭隘的流程优化，转向以增强人力、加速创新和创造新价值为核心的“工业前沿”。成功跨越这一前沿的企业聚焦两大关键：智能（AI需深度理解企业数据、工作流与知识）与信任（在高风险环境中确保安全、治理与可观测性）。缺乏智能，AI流于通用；缺乏信任，则规模化应用无从谈起。

制造业因其复杂的物理执行环境，成为“实体AI”的关键试验场。AI正从规划分析走向车间现场，协调设备、适应实时变化并与工人协同作业。这要求机器人、自主系统与AI智能体在动态环境中具备感知、决策和行动能力。“实体AI”旨在弥合传统自动化（强于重复、弱于适应）与人类工人（强于判断、受限于规模）之间的鸿沟，构建“人类主导、AI操作”的协同系统。

为实现“实体AI”的规模化部署，微软与英伟达正展开深度合作。英伟达提供包括加速计算、开放模型、仿真库及机器人框架在内的AI基础设施，使构建能够感知、推理和行动的自主机器人系统成为可能。微软则提供旨在安全、大规模运行“实体AI”的云与数据平台。双方合力帮助制造商将实验性方案转化为可用于生产全周期、工厂运营及供应链的成熟系统。

在“工业前沿”，AI被视为“数字同事”。当AI智能体基于运营数据、嵌入人类工作流并受全程治理时，可协助实时优化产线、协调维护与质量决策、应对供需波动、加速工程与产品周期决策。例如，制造商已开始利用基于仿真的AI智能体，在实地部署前虚拟评估生产变更，以降低风险、加快决策。关键之处在于，人类始终掌控系统：AI负责执行、监控与建议，人类负责设定目标、监督与裁决。

随着“实体AI”系统规模扩大，信任成为规模化成败的决定因素。制造商必须确保AI系统安全、可观测且在既定策略内运行，尤其在涉及安全关键流程时。治理不能事后补位，而必须内建于平台之中。前沿制造商将信任视为首要条件，让创新与可见性、合规及问责并行。

AI智能体、机器人、仿真与实时数据的融合，正将制造业推向转折点。曾经实验性的技术正在走向运营化，曾经孤立的部分正在实现连接。在即将到来的英伟达GTC 2026大会上，微软与英伟达将展示如何助力制造商当下部署、未来规模化扩展“实体AI”系统。对于制造业领导者而言，问题已不再是“实体AI”是否会重塑运营，而是如何以负责任的方式、内置信任地从一开始就加速并规模化应用这一变革性技术。

中文翻译：

赞助内容

实体人工智能为何成为制造业的下一个竞争优势

从仿真驱动开发到现实世界执行，微软与英伟达正携手助力制造业企业自信跨越工业前沿。

与微软及英伟达联合呈现

数十年来，制造业始终追求自动化以提升效率、降低成本并稳定运营。这一路径带来了显著收益，但如今已显不足。

当今的制造业领导者面临截然不同的挑战：如何在劳动力受限、复杂性攀升、且需在不牺牲安全、质量或信任的前提下加速创新的压力中实现增长。下一阶段的转型将不再由孤立的人工智能工具或单个机器人定义，而是取决于能在物理世界中可靠运行的智能。

这正是实体人工智能——即能在现实世界中感知、推理并行动的智能——标志着一个决定性转变的原因。也正因如此，微软与英伟达正通力合作，帮助制造业企业从实验探索迈向工业规模的生产应用。

工业前沿：智能与信任，而不仅是自动化

早期的人工智能应用多聚焦于局部优化：自动化任务、提升利用率、削减成本。尽管有价值，但这一阶段常引发新的摩擦，包括技能缺口、治理担忧以及对长期影响的不确定性。此外，应用场景虽多，却往往缺乏战略性。

工业前沿代表了一种不同的路径。前沿的制造企业不再追问机器能替代多少人力，而是探索人工智能如何扩展人类能力、加速创新、在保持可信与可控的同时，释放新型价值。

跨行业来看，成功迈入这一前沿阶段的企业都具备两项不可或缺的要素：

• 智能：人工智能系统必须理解企业实际如何处理其数据、工作流及机构知识。
• 信任：当人工智能开始在高风险环境中行动时，组织必须在每一层都保持安全性、治理能力和可观测性。

缺乏智能，人工智能将流于泛泛；缺乏信任，应用推广便会停滞。

为何制造业是实体人工智能的试验场

制造业独特地处于这场变革的中心。

人工智能不再局限于规划或分析领域，它正进入物理执行层面：协调机器、适应现实世界的多变性、并与工厂一线人员协同工作。机器人、自主系统及智能体如今必须在动态环境中感知、推理并行动。

这一转变暴露了一个关键缺口。传统自动化擅长重复性工作，却难以适应变化。人类工作者具备判断力和情境理解力，但受限于规模。实体人工智能通过构建人类主导、人工智能驱动的系统来弥合这一缺口——由人类设定意图，智能系统执行、学习并持续改进。人类是实现规模化成功的关键。

微软与英伟达：加速规模化实体人工智能应用

实体人工智能无法通过零散的点状解决方案实现。它需要由智能体驱动的、企业级的开发、部署和运营工具链与工作流，将仿真、数据、人工智能模型、机器人技术和治理连接成一个连贯的系统。

英伟达正在构建使实体人工智能成为可能的基础设施，包括加速计算、开放模型、仿真库，以及机器人技术框架和蓝图，赋能生态系统构建能在物理世界中感知、推理、规划并采取行动的自主机器人系统。微软则通过专为安全、大规模、跨企业运营实体人工智能而设计的云与数据平台，对此进行有力补充。

微软与英伟达携手，正助力制造业企业超越试点阶段，迈向可用于生产的实体人工智能系统。这些系统能够在涵盖产品生命周期、工厂运营和供应链的异构环境中进行开发、测试、部署并持续改进。

从智能到行动：工厂中的人机协作团队

在工业前沿，人工智能并非孤立系统，而是数字化的团队成员。

当人工智能智能体基于正确的运营数据、嵌入人类工作流程并实现端到端治理时，它们能够协助完成以下任务：

• 实时优化生产线
• 协调维护与质量决策
• 根据供应或需求中断调整运营
• 加速工程与产品生命周期决策

例如，制造商正开始使用基于仿真的AI智能体，在将生产变更部署到工厂车间之前进行虚拟评估，从而降低风险并加速决策。

关键在于，前沿制造商设计这些系统时，确保人类始终掌控主导权。人工智能负责执行、监控和提出建议，而人类则提供意图、监督和判断。这种平衡使组织能够更快行动，同时不失信心或控制力。

信任在实体人工智能规模化中的作用

随着实体人工智能系统规模化，信任成为关键制约因素。

制造商必须确保人工智能系统安全、可观测且在策略范围内运行，尤其是当它们影响安全关键或任务关键流程时。治理不能是事后补救措施，而必须内建于平台本身。

这就是为什么前沿制造商将信任视为首要要求，将创新与可见性、合规性和问责制紧密结合。唯有如此，实体人工智能才能从前景广阔的演示走向企业级的广泛部署。

为何此刻至关重要——以及未来展望

人工智能智能体、机器人技术、仿真与实时数据的融合，标志着制造业的一个拐点。曾经处于实验阶段的技术正走向实际运营；曾经孤立的部分正变得互联互通。

在NVIDIA GTC 2026大会上，微软与英伟达将展示此次合作如何支持实体人工智能系统——这些系统制造商如今即可部署，并能在未来负责任地扩展。从仿真驱动开发到现实世界执行，焦点始终在于助力制造业企业自信跨越工业前沿。

对于制造业领导者而言，问题不再是实体人工智能是否会重塑运营，而是他们能以多快的速度、负责任地、规模化地采用它，并从一开始就将信任构建其中。

在NVIDIA GTC 2026与微软一同探索更多。

本内容由微软制作，并非由《麻省理工科技评论》编辑团队撰写。

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Sponsored
Why physical AI is becoming manufacturing’s next advantage
From simulation‑driven development to real‑world execution, Microsoft and NVIDIA are helping manufacturers leverage AI to cross the industrial frontier with confidence.
In partnership withMicrosoft and NVIDIA
For decades, manufacturers have pursued automation to drive efficiency, reduce costs, and stabilize operations. That approach delivered meaningful gains, but it is no longer enough.
Today’s manufacturing leaders face a different challenge: how to grow amid labor constraints, rising complexity, and increasing pressure to innovate faster without sacrificing safety, quality, or trust. The next phase of transformation will not be defined by isolated AI tools or individual robots, but by intelligence that can operate reliably in the physical world.
This is where physical AI—intelligence that can sense, reason, and act in the real world—marks a decisive shift. And it is why Microsoft and NVIDIA are working together to help manufacturers move from experimentation to production at industrial scale.
The industrial frontier: Intelligence and trust, not just automation
Most early AI adoption focused on narrow optimization: automating tasks, improving utilization, and cutting costs. While valuable, that phase often created new friction, including skills gaps, governance concerns, and uncertainty about long‑term impact. Furthermore, the use cases were plentiful but not as strategic.
The industrial frontier represents a different approach. Rather than asking how much work machines can replace, frontier manufacturers ask how AI can expand human capability, accelerate innovation, and unlock new forms of value while remaining trustworthy and controllable.
Across industries, companies that successfully move into this frontier phase share two non‑negotiables:

• Intelligence: AI systems must understand how the business actually handles its data, workflows, and institutional knowledge.
• Trust: As AI begins to act in high‑stakes environments, organizations must retain security, governance, and observability at every layer.
  Without intelligence, AI becomes generic. Without trust, adoption stalls.
  Why manufacturing is the proving ground for physical AI
  Manufacturing is uniquely positioned at the center of this shift.
  AI is no longer confined to planning or analytics. It is moving into physical execution: coordinating machines, adapting to real‑world variability, and working alongside people on the factory floor. Robotics, autonomous systems, and AI agents must now perceive, reason, and act in dynamic environments.
  This transition exposes a critical gap. Traditional automation excels at repetition but struggles with adaptability. Human workers bring judgment and context but are constrained by scale. Physical AI closes that gap by enabling human‑led, AI‑operated systems, where people set intent and intelligent systems execute, learn, and improve over time. Humans are essential for scaled success.
  Microsoft and NVIDIA: Accelerating physical AI at scale
  Physical AI cannot be delivered through point solutions. It requires agentic-driven, enterprise-grade development, deployment, and operations toolchains and workflows that connect simulation, data, AI models, robotics, and governance into a coherent system.
  NVIDIA is building the AI infrastructure that makes physical AI possible, including accelerated computing, open models, simulation libraries, and robotics frameworks and blueprints that enable the ecosystem to build autonomous robotics systems that can perceive, reason, plan, and take action in the physical world. Microsoft complements this with a cloud and data platform designed to operate physical AI securely, at scale, and across the enterprise.
  Together, Microsoft and NVIDIA are enabling manufacturers to move beyond pilots toward production‑ready physical AI systems that can be developed, tested, deployed, and continuously improved across heterogeneous environments spanning the product lifecycle, factory operations, and supply chain.
  From intelligence to action: Human-agent teams in the factory
  At the industrial frontier, AI is not a standalone system, but a digital teammate.
  When AI agents are grounded in the proper operational data, embedded in human workflows, and governed end to end, they can assist with tasks such as:
• Optimizing production lines in real time
• Coordinating maintenance and quality decisions
• Adapting operations to supply or demand disruptions
• Accelerating engineering and product lifecycle decisions
  For example, manufacturers are beginning to use simulation‑grounded AI agents to evaluate production changes virtually before deploying them on the factory floor, reducing risk while accelerating decision‑making.
  Crucially, frontier manufacturers design these systems so humans remain in control. AI executes, monitors, and recommends, while people provide intent, oversight, and judgment. This balance allows organizations to move faster without losing confidence or control.
  The role of trust in scaling physical AI
  As physical AI systems scale, trust becomes the limiting factor.
  Manufacturers must ensure that AI systems are secure, observable, and operating within policy, especially when they influence safety‑critical or mission‑critical processes. Governance cannot be an afterthought; It must be engineered into the platform itself.
  This is why frontier manufacturers treat trust as a first‑class requirement, pairing innovation with visibility, compliance, and accountability. Only then can physical AI move from promising demonstrations to enterprise‑wide deployment.
  Why this moment matters—and what’s next
  The convergence of AI agents, robotics, simulation, and real‑time data marks an inflection point for manufacturing. What was once experimental is becoming operational. What was once siloed is becoming connected.
  At NVIDIA GTC 2026, Microsoft and NVIDIA will demonstrate how this collaboration supports physical AI systems that manufacturers can deploy today and scale responsibly tomorrow. From simulation‑driven development to real‑world execution, the focus is on helping manufacturers cross the industrial frontier with confidence.
  For manufacturing leaders, the question is no longer whether physical AI will reshape operations, but how quickly they can adopt it responsibly, at scale, and with trust built in from the start.
  Discover more with Microsoft at NVIDIA GTC 2026.
  This content was produced by Microsoft. It was not written by MIT Technology Review’s editorial staff.
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