为人工智能构建网络：奠定实时智能的基石
内容总结：
在2025年纽约贝斯佩奇黑色球场举办的莱德杯高尔夫锦标赛中，近25万名观众亲临现场，见证了这场横跨近一个世纪的欧美高尔夫对决。这场赛事的成功不仅取决于运动员的技艺，更依托于一套能够支撑实时人工智能决策的高性能网络基础设施。
赛事与技术合作伙伴慧与公司（HPE）共同搭建了智能运营中心，通过聚合门票扫描、气象数据、移动球车GPS定位、销售终端及67个AI摄像头的实时信息，构建了动态可视化的指挥看板。该平台依托超过650个WiFi 6E接入点与170台网络交换机，在临时数据中心内组建了具备超低延迟特性的私有云AI算力集群，实现了对人员流动、设施状态与网络负载的秒级响应。
HPE网络业务首席技术官乔恩·格林指出：“当业界将目光聚焦于算法与数据时，支撑实时推理的网络架构成为AI落地的关键支柱。离散的AI系统价值有限，必须通过高速网络实现数据输入与决策输出的闭环。” 调查显示，尽管今年具备实时数据推送能力的企业比例从7%跃升至45%，但过半机构仍在数据管道落地环节面临挑战。
这一实践印证了“推理就绪网络”的核心特征：需具备无损传输、超低延时及动态扩展能力。与传统企业网络不同，AI运算要求在多GPU间实现海量数据的精准同步，任何微小延迟都将直接影响整体效能。
随着物理AI（Physical AI）在智能制造、自动驾驶等场景普及，边缘计算正推动 workloads 从云端回归本地。格林预测：“当工厂机械臂需要毫秒级响应时，云端往返传输将无法满足安全需求。这正是算力回归本地的驱动力。”IDC数据显示，AI基础设施市场规模预计在2029年达到7580亿美元。
与此同时，AI也在反哺网络进化。HPE通过分析全球数千亿设备遥测数据，构建出具备自愈能力的“自动驾驶网络”雏形——系统可自动配置百余台交换机，检测端口异常并实施修复。这种双向赋能关系正在重塑企业竞争力：无论是大型活动调度还是供应链优化，网络性能正成为决定商业效能的新基石。
（本文由MIT Technology Review定制内容团队创作，HPE参与合作）
中文翻译：
赞助内容
AI网络建设：为实时智能奠定基石
具备AI推理能力的网络是将人工智能潜力转化为实际效能的关键基础设施。
本文由HPE赞助撰写
莱德杯是一项拥有近百年历史的赛事，是欧洲与美国在高尔夫球技与策略上的精英对决。在2025年的赛事中，近25万名观众齐聚一堂，观看了为期三天的激烈角逐。
从技术和物流的角度来看，成功举办如此规模的赛事绝非易事。莱德杯的基础设施必须能够容纳每天涌入赛场（今年位于纽约法明代尔的黑球场）的数万名网络用户。
为应对如此复杂的IT挑战，莱德杯赛事方与技术合作伙伴HPE携手，为其运营创建了一个中央枢纽。该解决方案的核心是一个平台，赛事工作人员可通过该平台访问数据可视化界面，以支持运营决策。这个仪表板依托高性能网络和私有云环境，汇总并提炼了来自各种实时数据源的洞察。
这让我们得以一窥大规模AI就绪网络的模样——这是一次现实世界的压力测试，其影响范围从赛事管理到企业运营，无所不包。HPE网络业务首席技术官乔恩·格林解释道，尽管模型和数据就绪性占据了董事会关注和媒体宣传的绝大部分，但网络是成功实施AI的第三个关键支柱。"孤立的AI作用有限；你需要一种方式，在训练和推理过程中，将数据输入AI并从中获取结果，"他表示。
随着企业迈向分布式、实时AI应用，未来的网络将需要以更快的速度解析更海量的信息。在黑球场的绿茵场上所展现的一切，为各行各业提供了一个经验：具备推理能力的网络，是将AI承诺转化为现实表现的决定性因素。
打造具备AI推理能力的网络
超过半数的组织仍在努力使其数据管道投入运营。在HPE近期一项涉及1775名IT决策者的跨行业调查中，仅有45%的受访者表示能够为创新项目运行实时的数据推送和拉取。与去年的数据（2024年仅有7%表示具备此能力）相比，这是一个显著的变化，但在连接数据收集与实时决策方面，仍有工作待完成。
网络可能是进一步缩小这一差距的关键。部分解决方案可能取决于基础设施设计。传统的企业网络旨在处理可预测的业务应用流量——如电子邮件、浏览器、文件共享等——但其设计并非为了应对AI工作负载所需的动态、高容量数据移动。特别是推理任务，依赖于在多个GPU之间以类似超级计算机的精度传输海量数据集。
"对于标准的、现成的企业网络，或许可以容忍一些性能和延迟上的波动，"格林说，"如果电子邮件平台慢了半秒，很少有人会注意到。但对于AI事务处理而言，整个任务的完成速度取决于最后一步计算。因此，任何数据包丢失或网络拥塞都会变得非常明显。"
因此，为AI构建的网络必须具备一系列不同的性能特征，包括超低延迟、无损吞吐量、专用设备以及大规模适应性。其中一点不同在于AI的分布式特性，这影响了数据的无缝流动。
莱德杯生动地展示了这类新型网络的实战应用。赛事期间，设立了一个"互联智能中心"，用于接收来自门票扫描、天气报告、GPS追踪的高尔夫球车、餐饮和商品销售、观众及消费者队列以及网络性能的数据。此外，整个球场还部署了67个具备AI功能的摄像头。这些输入通过运营智能仪表板进行分析，为工作人员提供整个场地活动的即时视图。
"从网络角度来看，这项赛事非常复杂，因为有许多开阔区域，人员分布并不均匀，"格林解释道，"人们倾向于跟随比赛进程移动。因此，在某些区域，人员和设备密度极高，而其他区域则完全空无一人。"
为应对这种变化性，工程师们构建了一个双层架构。在整个广阔的场馆中，超过650个WiFi 6E接入点、170台网络交换机和25个用户体验传感器协同工作，以维持持续连接，并为私有云AI集群提供实时分析数据。前端层连接摄像头、传感器和接入点，以捕获实时视频和移动数据；而后端层——位于临时的现场数据中心内——则以高速、低延迟的配置连接GPU和服务器，这实际上充当了系统的大脑。整个设置共同实现了快速的现场响应以及可为未来运营规划提供信息的数据收集。"团队还可以使用AI模型来处理拍摄的视频片段，并帮助从影像中确定哪些是最精彩的击球，"格林补充道。
实体AI与现场智能的回归
如果说时间对于赛事管理至关重要，那么在涉及安全问题的场景下——例如自动驾驶汽车需要在瞬间做出加速或刹车的决定——时间就更为关键了。
为应对实体AI的兴起（即AI应用从屏幕走向工厂车间和城市街道），越来越多的企业正在重新思考其架构。一些企业不再将数据发送到集中式云进行推理，而是部署基于边缘的AI集群，在更接近数据生成地的地方处理信息。数据密集型的训练可能仍在云端进行，但推理则在现场完成。
这种混合方法正推动一波运营回流浪潮，曾经被委派到云端的工作负载，出于速度、安全性、数据主权和成本的考虑，正回归本地基础设施。"近年来，我们经历了IT向云端的迁移，但我们相信，实体AI将是促使大量工作负载回归本地的一个用例，"格林预测道，他举了一个AI赋能的工厂车间为例，传感器数据往返云端的延迟对于安全控制自动化机械而言过于缓慢。"等到在云端完成处理时，机器已经移动了，"他解释道。
有数据支持格林的预测：企业研究集团的研究显示，84%的受访者因AI的增长正在重新评估应用部署策略。市场预测也反映了这一转变。根据IDC的数据，AI基础设施市场预计到2029年将达到7580亿美元。
AI赋能网络与自动驾驶式基础设施的未来
网络与AI的关系是循环互促的：现代网络使大规模AI成为可能，而AI也在帮助网络变得更智能、更强大。
"网络是任何组织中数据最丰富的系统之一，"格林说，"这使其成为AI的完美应用场景。我们可以分析数千个客户环境中的数百万个配置状态，并了解真正能提高性能或稳定性的因素。"
例如，HPE拥有世界上最大的网络遥测数据库之一，其AI模型分析从数十亿联网设备收集的匿名化数据，以识别趋势并随时间推移优化行为。该平台每天处理超过万亿个遥测点，这意味着它可以持续从现实世界条件中学习。
broadly known as AIOps（或AI驱动的IT运营）的概念正在改变各行各业管理企业网络的方式。如今，AI将洞察以建议的形式呈现，管理员只需单击一下即可选择应用。未来，这些系统或许能自动测试并自行部署低风险的变更。
格林指出，那个长远愿景被称为"自动驾驶网络"——一个能够处理历来困扰IT团队的重复性、易出错任务的网络。"AI不会取代网络工程师的工作，但它将消除那些拖慢他们速度的繁琐工作，"他说，"你将能够下令，'请去配置130台交换机来解决这个问题'，系统就会处理。当一个端口卡住，或者有人插错了连接器方向时，AI能够检测到——并且在许多情况下，自动修复它。"
结语
数字化举措的成败如今取决于信息流动的效率。无论是协调现场活动还是优化供应链，网络的性能日益决定着企业的绩效。今天打好这个基础，将区分出那些能够成功试点AI与那些能够规模化部署AI的企业。
欲了解更多信息，请注册观看《麻省理工科技评论》举办的EmTech AI沙龙，本期特邀HPE。
本文由《麻省理工科技评论》的定制内容部门Insights制作。并非由《麻省理工科技评论》的编辑人员撰写。内容由人类作者、编辑、分析师和插画师进行调研、设计和撰写。这包括调查问卷的编写和数据收集。可能使用过的AI工具仅限于经过严格人工审查的次要生产流程。
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Networking for AI: Building the foundation for real-time intelligence
AI inference-ready networks are essential infrastructure for turning AI’s potential into performance.
In partnership withHPE
The Ryder Cup is an almost-century-old tournament pitting Europe against the United States in an elite showcase of golf skill and strategy. At the 2025 event, nearly a quarter of a million spectators gathered to watch three days of fierce competition on the fairways.
From a technology and logistics perspective, pulling off an event of this scale is no easy feat. The Ryder Cup’s infrastructure must accommodate the tens of thousands of network users who flood the venue (this year, at Bethpage Black in Farmingdale, New York) every day.
To manage this IT complexity, Ryder Cup engaged technology partner HPE to create a central hub for its operations. The solution centered around a platform where tournament staff could access data visualization supporting operational decision-making. This dashboard, which leveraged a high-performance network and private-cloud environment, aggregated and distilled insights from diverse real-time data feeds.
It was a glimpse into what AI-ready networking looks like at scale—a real-world stress test with implications for everything from event management to enterprise operations. While models and data readiness get the lion's share of boardroom attention and media hype, networking is a critical third leg of successful AI implementation, explains Jon Green, CTO of HPE Networking. “Disconnected AI doesn’t get you very much; you need a way to get data into it and out of it for both training and inference,” he says.
As businesses move toward distributed, real-time AI applications, tomorrow’s networks will need to parse even more massive volumes of information at ever more lightning-fast speeds. What played out on the greens at Bethpage Black represents a lesson being learned across industries: Inference-ready networks are a make-or-break factor for turning AI’s promise into real-world performance.
Making a network AI inference-ready
More than half of organizations are still struggling to operationalize their data pipelines. In a recent HPE cross-industry survey of 1,775 IT leaders, 45% said they could run real-time data pushes and pulls for innovation. It’s a noticeable change over last year’s numbers (just 7% reported having such capabilities in 2024), but there’s still work to be done to connect data collection with real-time decision-making.
The network may hold the key to further narrowing that gap. Part of the solution will likely come down to infrastructure design. While traditional enterprise networks are engineered to handle the predictable flow of business applications—email, browsers, file sharing, etc.—they're not designed to field the dynamic, high-volume data movement required by AI workloads. Inferencing in particular depends on shuttling vast datasets between multiple GPUs with supercomputer-like precision.
“There’s an ability to play fast and loose with a standard, off-the-shelf enterprise network,” says Green. “Few will notice if an email platform is half a second slower than it might’ve been. But with AI transaction processing, the entire job is gated by the last calculation taking place. So it becomes really noticeable if you’ve got any loss or congestion.”
Networks built for AI, therefore, must operate with a different set of performance characteristics, including ultra-low latency, lossless throughput, specialized equipment, and adaptability at scale. One of these differences is AI’s distributed nature, which affects the seamless flow of data.
The Ryder Cup was a vivid demonstration of this new class of networking in action. During the event, a Connected Intelligence Center was put in place to ingest data from ticket scans, weather reports, GPS-tracked golf carts, concession and merchandise sales, spectator and consumer queues, and network performance. Additionally, 67 AI-enabled cameras were positioned throughout the course. Inputs were analyzed through an operational intelligence dashboard and provided staff with an instantaneous view of activity across the grounds.
"The tournament is really complex from a networking perspective, because you have many big open areas that aren't uniformly packed with people," explains Green. "People tend to follow the action. So in certain areas, it's really dense with lots of people and devices, while other areas are completely empty."
To handle that variability, engineers built out a two-tiered architecture. Across the sprawling venue, more than 650 WiFi 6E access points, 170 network switches, and 25 user experience sensors worked together to maintain continuous connectivity and feed a private cloud AI cluster for live analytics. The front-end layer connected cameras, sensors, and access points to capture live video and movement data, while a back-end layer—located within a temporary on-site data center—linked GPUs and servers in a high-speed, low-latency configuration that effectively served as the system’s brain. Together, the setup enabled both rapid on-the-ground responses and data collection that could inform future operational planning. "AI models also were available to the team which could process video of the shots taken and help determine, from the footage, which ones were the most interesting,” says Green.
Physical AI and the return of on-prem intelligence
If time is of the essence for event management, it's even more critical in contexts where safety is on the line—for instance a self-driving car making a split-second decision to accelerate or brake.
In planning for the rise of physical AI, where applications move off screens and onto factory floors and city streets, a growing number of enterprises are rethinking their architectures. Instead of sending the data to centralized clouds for inference, some are deploying edge-based AI clusters that process information closer to where it is generated. Data-intensive training may still occur in the cloud, but inferencing happens on-site.
This hybrid approach is fueling a wave of operational repatriation, as workloads once relegated to the cloud return to on-premises infrastructure for enhanced speed, security, sovereignty, and cost reasons. "We’ve had an out-migration of IT into the cloud in recent years, but physical AI is one of the use cases that we believe will bring a lot of that back on-prem," predicts Green, giving the example of an AI-infused factory floor, where a round-trip of sensor data to the cloud would be too slow to safely control automated machinery. "By the time processing happens in the cloud, the machine has already moved," he explains.
There's data to back up Green's projection: research from Enterprise Research Group shows that 84% of respondents are reevaluating application deployment strategies due to the growth of AI. Market forecasts also reflect this shift. According to IDC, the AI market for infrastructure is expected to reach $758 billion by 2029.
AI for networking and the future of self-driving infrastructure
The relationship between networking and AI is circular: Modern networks make AI at scale possible, but AI is also helping make networks smarter and more capable.
“Networks are some of the most data-rich systems in any organization,” says Green. “That makes them a perfect use case for AI. We can analyze millions of configuration states across thousands of customer environments and learn what actually improves performance or stability.”
At HPE for example, which has one of the largest network telemetry repositories in the world, AI models analyze anonymized data collected from billions of connected devices to identify trends and refine behavior over time. The platform processes more than a trillion telemetry points each day, which means it can continuously learn from real-world conditions.
The concept broadly known as AIOps (or AI-driven IT operations) is changing how enterprise networks are managed across industries. Today, AI surfaces insights as recommendations that administrators can choose to apply with a single click. Tomorrow, those same systems might automatically test and deploy low-risk changes themselves.
That long-term vision, Green notes, is referred to as a “self-driving network”—one that handles the repetitive, error-prone tasks that have historically plagued IT teams. “AI isn’t coming for the network engineer’s job, but it will eliminate the tedious stuff that slows them down," he says. "You’ll be able to say, ‘Please go configure 130 switches to solve this issue,’ and the system will handle it. When a port gets stuck or someone plugs a connector in the wrong direction, AI can detect it—and in many cases, fix it automatically.”
Digital initiatives now depend on how effectively information moves. Whether coordinating a live event or streamlining a supply chain, the performance of the network increasingly defines the performance of the business. Building that foundation today will separate those who pilot from those who scale AI.
For more, register to watch MIT Technology Review's EmTech AI Salon, featuring HPE.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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