<p>Arm 对 2026 年的最新预测清晰描绘了人工智能的未来，标志着计算能力在全球范围内部署和利用的深刻转变。根据公告，该公司预见到一个智能计算的新纪元，决定性地超越集中式云计算，迈向一种无处不在的、分布式的智能，覆盖每一个设备、表面和系统。这一转变强调模块化、能效和云、边缘及物理人工智能环境之间的无缝连接，根本重塑技术格局和用户互动。</p>
<p>基础性的变化始于硅层面，创新正在超越传统的晶体管缩放。模块化芯片将重新定义硅设计，允许通过混合工艺节点和集成专用人工智能引擎，定制适用于多种人工智能工作负载的经济高效的系统级芯片。这种 “超越摩尔定律” 的演变，由先进材料和三维集成驱动，优先考虑垂直堆叠以提高密度和效率，成为维持高性能、能效计算进步的关键，并为更强大的人工智能系统奠定基础。同时，具有硬件级信任和保密计算的安全设计硅，将从商业差异化转变为普遍需求，随着人工智能渗透到关键基础设施中，保护敏感数据和操作免受日益复杂的对手的威胁。专用加速的兴起，与其软件栈在系统层面共同设计，进一步强调了这一硬件演变，导致针对特定人工智能框架优化的专用平台的出现，以及最大化每单位面积计算能力的融合人工智能数据中心的出现。</p>
<p>随着硬件的发展，人工智能的部署也在演变，将智能推向数据源附近，促进协作的连续性。分布式人工智能计算将越来越多地将推理处理从云迁移到边缘设备，使得得益于算法进步和专用硅的复杂模型能够更快响应和实时适应。这种云、边缘和物理人工智能的融合将使系统作为一个协调的连续体运行，任务将智能地路由到最合适的层次，以进行大规模训练、低延迟感知或现实世界执行。其影响深远：自主和自主的人工智能将从单纯的助手转变为机器人、车辆和物流中的自给自足的代理，而设备上的上下文人工智能将预测用户需求，以前所未有的精确度、个性化和隐私定制体验。此外，行业将朝着许多专用的小型语言模型（SLMs）发展，优化特定领域并足够高效以适应受限电源的边缘环境，标志着与 “一个巨型模型” 范式的显著背离，使得先进的人工智能对小型企业更加可及。</p>
<h2>物理人工智能与无处不在的智能织物</h2>
<p>这些进步的影响将在物理世界和用户体验中最为明显，人工智能将带来巨大的生产力提升，并重新定义人机交互。世界模型将成为开发和验证物理人工智能系统的不可或缺的工具，为训练自主机器和机器人创建丰富的虚拟环境，以便在现实世界部署之前，降低风险并加速开发周期。这种物理人工智能的扩展，由多模态模型的突破和更高效的训练与推理管道驱动，将重塑从医疗到制造等各个行业，使得在危险或低效环境中实现自主操作。同时，传统设备类别（如个人电脑、移动设备和物联网）之间长期存在的界限将消失，取而代之的是统一的、设备无关的计算织物。这种 “人工智能个人织物” 将连接每一个设备，使智能能够与用户流畅移动，在屏幕和传感器之间共享上下文和学习，提供无缝、个性化的体验，日常连接设备将为这一智能生态系统做出贡献。例如，医疗可穿戴设备将从健身伴侣演变为临床级诊断工具，利用本地人工智能模型进行实时生物特征分析和远程患者监测，改变持续护理和早期检测。</p>
<p>最终，Arm 对人工智能未来的愿景集中在无处不在、高效的智能上，强调每瓦特智能将是人工智能模型的核心基准。贯穿所有这些预测的共同线索是对先进每瓦特智能的持续关注，确保人工智能能力不仅强大，而且在每一个可想象的终端（从数据中心到最小的物联网设备）上都是可持续和可及的。这一战略重点使 Arm 成为下一波创新的关键推动者，为一个日益智能、可扩展和安全的世界提供基础计算平台。未来几年无疑将展示这一分布式人工智能范式的变革力量，使智能成为我们日常生活的一个环境性、不可或缺的部分。</p>

<p>Arm 预测到 2026 年将出现向分布式智能的转变，超越集中式云计算，实现设备间的普遍人工智能。这涉及模块化芯片、设计安全的硅材料和专门的人工智能平台。人工智能将从云端转向边缘计算，增强实时响应和个性化服务。行业将采用小型语言模型用于特定领域，使人工智能对小型企业更具可及性。Arm 强调每瓦特智能作为关键基准，确立其在可扩展、安全的人工智能创新中的领导地位</p>

<p>Arm’s latest predictions for 2026 paint a clear picture of the Future of AI, signaling a profound shift in how compute power will be deployed and utilized across the globe. According to the announcement, the company anticipates a new era of intelligent computing, moving decisively beyond centralized clouds to a pervasive, distributed intelligence spanning every device, surface, and system imaginable. This transformation emphasizes modularity, power efficiency, and seamless connectivity across cloud, edge, and physical AI environments, fundamentally reshaping technological landscapes and user interactions.</p>
<div class="lb-trans"><p>Arm 对 2026 年的最新预测清晰描绘了人工智能的未来，标志着计算能力在全球范围内部署和利用的深刻转变。根据公告，该公司预见到一个智能计算的新纪元，决定性地超越集中式云计算，迈向一种无处不在的、分布式的智能，覆盖每一个设备、表面和系统。这一转变强调模块化、能效和云、边缘及物理人工智能环境之间的无缝连接，根本重塑技术格局和用户互动。</p>
</div><p>The foundational shifts begin at the silicon level, where innovation is accelerating beyond traditional transistor scaling. Modular chiplets are poised to redefine silicon design, allowing for customizable, cost-effective systems-on-chip tailored for diverse AI workloads by mixing process nodes and integrating specialized AI engines. This “More-than-Moore” evolution, driven by advanced materials and 3D integration, prioritizes vertical stacking for higher density and efficiency, becoming essential for sustaining progress in high-performance, energy-efficient computing and laying the groundwork for more capable AI systems. Concurrently, secure-by-design silicon, featuring hardware-level trust and confidential compute, will shift from a commercial differentiator to a universal requirement as AI permeates critical infrastructure, safeguarding sensitive data and operations against increasingly sophisticated adversaries. The rise of specialized acceleration, co-designed with their software stacks at a system-level, further underscores this hardware evolution, leading to purpose-built platforms optimized for particular AI frameworks and the emergence of converged AI data centers that maximize compute per unit of area.</p>
<div class="lb-trans"><p>基础性的变化始于硅层面，创新正在超越传统的晶体管缩放。模块化芯片将重新定义硅设计，允许通过混合工艺节点和集成专用人工智能引擎，定制适用于多种人工智能工作负载的经济高效的系统级芯片。这种 “超越摩尔定律” 的演变，由先进材料和三维集成驱动，优先考虑垂直堆叠以提高密度和效率，成为维持高性能、能效计算进步的关键，并为更强大的人工智能系统奠定基础。同时，具有硬件级信任和保密计算的安全设计硅，将从商业差异化转变为普遍需求，随着人工智能渗透到关键基础设施中，保护敏感数据和操作免受日益复杂的对手的威胁。专用加速的兴起，与其软件栈在系统层面共同设计，进一步强调了这一硬件演变，导致针对特定人工智能框架优化的专用平台的出现，以及最大化每单位面积计算能力的融合人工智能数据中心的出现。</p>
</div><p>As hardware evolves, so does the deployment of AI itself, pushing intelligence closer to the data source and fostering a collaborative continuum. Distributed AI compute will increasingly migrate inference processing from the cloud to edge devices, enabling quicker responses and real-time adaptation with complex models thanks to algorithmic advancements and specialized silicon. This convergence of cloud, edge, and physical AI will see systems operating as a coordinated continuum, with tasks intelligently routed to the most suitable layer for large-scale training, low-latency perception, or real-world execution. The implications are vast: agentic and autonomous AI will transition from mere assistants to self-sufficient agents across robotics, vehicles, and logistics, while contextual AI on devices will anticipate user needs, tailoring experiences with unprecedented precision, personalization, and privacy. Furthermore, the industry will move towards many purpose-built Small Language Models (SLMs), optimized for specific domains and efficient enough for power-constrained edge environments, marking a significant departure from the “one giant model” paradigm and making advanced AI more accessible to smaller enterprises.</p>
<div class="lb-trans"><p>随着硬件的发展，人工智能的部署也在演变，将智能推向数据源附近，促进协作的连续性。分布式人工智能计算将越来越多地将推理处理从云迁移到边缘设备，使得得益于算法进步和专用硅的复杂模型能够更快响应和实时适应。这种云、边缘和物理人工智能的融合将使系统作为一个协调的连续体运行，任务将智能地路由到最合适的层次，以进行大规模训练、低延迟感知或现实世界执行。其影响深远：自主和自主的人工智能将从单纯的助手转变为机器人、车辆和物流中的自给自足的代理，而设备上的上下文人工智能将预测用户需求，以前所未有的精确度、个性化和隐私定制体验。此外，行业将朝着许多专用的小型语言模型（SLMs）发展，优化特定领域并足够高效以适应受限电源的边缘环境，标志着与 “一个巨型模型” 范式的显著背离，使得先进的人工智能对小型企业更加可及。</p>
</div><h2>Physical AI and the Pervasive Intelligent Fabric</h2>
<div class="lb-trans"><h2>物理人工智能与无处不在的智能织物</h2>
</div><p>The impact of these advancements will be most tangible in the physical world and user experience, where AI is set to deliver massive productivity gains and redefine human-device interaction. World models will become indispensable tools for developing and validating physical AI systems, creating rich virtual environments for training autonomous machines and robots before real-world deployment, thereby reducing risk and accelerating development cycles. This scaling of physical AI, driven by breakthroughs in multimodal models and more efficient training and inference pipelines, will reshape industries from healthcare to manufacturing, enabling autonomous operations in dangerous or inefficient environments. Simultaneously, the long-standing divisions between traditional device categories like PCs, mobile, and IoT will dissolve, giving way to a unified, device-agnostic computing fabric. This “AI personal fabric” will connect every device, allowing intelligence to move fluidly with users, sharing context and learning across screens and sensors to deliver seamless, personalized experiences, with everyday connected devices contributing to this intelligent ecosystem. Healthcare wearables, for instance, will evolve from fitness companions into clinical-grade diagnostic tools, leveraging local AI models for real-time biometric analysis and remote patient monitoring, transforming continuous care and early detection.</p>
<div class="lb-trans"><p>这些进步的影响将在物理世界和用户体验中最为明显，人工智能将带来巨大的生产力提升，并重新定义人机交互。世界模型将成为开发和验证物理人工智能系统的不可或缺的工具，为训练自主机器和机器人创建丰富的虚拟环境，以便在现实世界部署之前，降低风险并加速开发周期。这种物理人工智能的扩展，由多模态模型的突破和更高效的训练与推理管道驱动，将重塑从医疗到制造等各个行业，使得在危险或低效环境中实现自主操作。同时，传统设备类别（如个人电脑、移动设备和物联网）之间长期存在的界限将消失，取而代之的是统一的、设备无关的计算织物。这种 “人工智能个人织物” 将连接每一个设备，使智能能够与用户流畅移动，在屏幕和传感器之间共享上下文和学习，提供无缝、个性化的体验，日常连接设备将为这一智能生态系统做出贡献。例如，医疗可穿戴设备将从健身伴侣演变为临床级诊断工具，利用本地人工智能模型进行实时生物特征分析和远程患者监测，改变持续护理和早期检测。</p>
</div><p>Ultimately, Arm’s vision for the Future of AI centers on pervasive, efficient intelligence, emphasizing that intelligence-per-watt will be the central benchmark for AI models. The common thread woven through all these predictions is a relentless focus on advanced intelligence-per-watt, ensuring that AI capabilities are not only powerful but also sustainable and accessible across every conceivable endpoint, from data centers to the smallest IoT device. This strategic emphasis positions Arm as a critical enabler for the next wave of innovation, providing the foundational compute platform for an increasingly intelligent, scalable, and secure world. The coming years will undoubtedly demonstrate the transformative power of this distributed AI paradigm, making intelligence an ambient, integral part of our daily lives.</p>
<div class="lb-trans"><p>最终，Arm 对人工智能未来的愿景集中在无处不在、高效的智能上，强调每瓦特智能将是人工智能模型的核心基准。贯穿所有这些预测的共同线索是对先进每瓦特智能的持续关注，确保人工智能能力不仅强大，而且在每一个可想象的终端（从数据中心到最小的物联网设备）上都是可持续和可及的。这一战略重点使 Arm 成为下一波创新的关键推动者，为一个日益智能、可扩展和安全的世界提供基础计算平台。未来几年无疑将展示这一分布式人工智能范式的变革力量，使智能成为我们日常生活的一个环境性、不可或缺的部分。</p>
</div>

The Future of AI: Arm’s Vision for Distributed Intelligence

- Arm 预测未来 AI 将实现分布式智能，超越集中式云计算。  
- 硅级创新和模块化芯片将提升算力和效率，满足多样 AI 需求。  
- AI 将融入日常生活，推动各行业的自动化和智能化转型。

人工智能的未来：Arm 对分布式智能的愿景