云计算“隐形”了？别闹！出海人，你的钱袋子准备好了吗？

哎呀，各位出海弄潮儿们，我是你们的老朋友雨生，人称“云计算界的Corey Quinn”（虽然我头发比他多）。今天咱来聊聊云计算这事儿，都2025年了，没想到它竟然“隐形”了！这可不是什么魔术，而是因为它已经像空气一样，无处不在，却又常常被人忽略。

**标题：云计算“隐形”了？别闹！出海人，你的钱袋子准备好了吗？**

**导语：** 云计算都“隐形”了？难道以后要改名叫“数据处理”？雨生我掐指一算，这背后藏着大大的商机！出海的兄弟姐妹们，抓住这波红利，下一个独角兽就是你！

---

**雨生视角：这哪是“隐形”，分明是“进化”！**

文章里说，云计算现在太普遍了，就像当年的小型机一样，用着用着就忘了它的存在。哼，这帮老外就是喜欢玩文字游戏！依我看，这哪是“隐形”，分明是“进化”！云计算已经渗透到我们出海的方方面面，从市场调研到客户管理，从产品推广到售后服务，哪个环节离得开它？

“Cloud computing has become so normal, it's invisible. Maybe someday we'll just call it 'data processing' again.”

“云计算已经变得如此普遍，它已经隐形了。也许有一天我们会再次称它为“数据处理”。

想当年，咱们为了服务器，那可是跑断了腿，还得担心机房停电、硬盘坏掉。现在呢？鼠标一点，云服务器立马就位，弹性伸缩，按需付费，简直不要太爽！

**深度解读：IDC数据告诉你，风口在哪儿！**

文章里提到了IDC的数据，说2024年共享云支出猛增，主要原因是GenAI（生成式AI）的爆发。这说明什么？说明AI才是云计算的真正推手！

“There was a boom in shared cloud spending in 2024, thanks in large part to the GenAI boom, and this part of the IT infrastructure market continues to grow nicely in 2025 according to the forecast.”

“2024年共享云支出出现繁荣，这在很大程度上归功于 GenAI 的繁荣，根据预测，IT 基础设施市场的这一部分将在 2025 年继续良好增长。”

(The Register)

别光顾着看热闹，赶紧想想你的业务能不能和AI结合起来！比如，用AI做海外市场的用户画像分析，用AI生成多语种的产品文案，甚至可以用AI来优化你的广告投放策略。

另外，IDC还预测，未来几年专用云的增长速度将超过共享云。这意味着什么？意味着越来越多的企业开始重视数据的安全性和合规性，需要更定制化的云计算解决方案。

**行动指南：出海，就要玩转云！**

*   **降本增效：** 别再死抱着传统IT架构不放了！赶紧拥抱云计算，按需付费，弹性伸缩，省下来的钱可以多请几个漂亮小姐姐/小哥哥！
*   **拓展市场：** 利用云计算的全球覆盖能力，快速部署海外业务，轻松触达全球客户。
*   **提升竞争力：** 借助云计算的AI能力，提升产品智能化水平，让你的产品在海外市场脱颖而出。

**互动环节：**

各位出海大佬们，你们觉得云计算“隐形”了吗？你们在出海过程中，都用云计算做了哪些骚操作？欢迎在评论区分享你的观点，雨生我等你来吐槽！

**雨生的VIP朋友们：出海，你不是一个人在战斗！**

想了解更多出海干货，想和更多出海大佬交流？欢迎加入雨生的知识星球 “雨生的VIP朋友们”！这里有：

*   **独家报告：** 雨生亲自撰写的深度行业报告，让你掌握最前沿的出海趋势。
*   **专家问答：** 遇到出海难题？雨生和星球里的各路专家为你答疑解惑。
*   **社群交流：** 和众多出海精英交流经验，拓展人脉，抱团取暖。
*   **海外资源对接：** 助你对接海外渠道、投资人、合作伙伴，加速出海进程。

现在加入，还可以享受3天免费试用！ 甚至有机会获得专属折扣！点击这里了解详情 [ * 添加知识星球链接* ]



---

**金句海报：** (3-5条)

1.  云计算“隐形”了？别闹！出海人，你的钱袋子准备好了吗？ #雨生云计算 #出海必读
2.  AI才是云计算的真正推手！ #雨生云计算 #出海必读 #知识星球
3.  出海，就要玩转云！ #雨生云计算 #出海必读
4.  加入雨生的知识星球，出海你不是一个人在战斗！#雨生云计算 #知识星球

**话题标签：**

#雨生云计算# #出海必读# #知识星球# #云计算# #AI# #数字化转型# #出海#

**邀请关注有礼活动方案：**

*   **活动主题：** 邀请好友，共赢出海！
*   **活动规则：** 邀请3位好友关注“雨生云计算”公众号，即可获得知识星球3天免费体验券；邀请5位好友关注，即可获得知识星球专属折扣券。
*   **奖品设置：**
   *   知识星球3天免费体验券
   *   知识星球专属折扣券（原价550元/年）

**朋友圈文案模板：**

1.  雨生大佬的最新解读，出海必读！云计算“隐形”了？这背后藏着大大的商机！强烈推荐加入知识星球，干货满满！[文章链接] #雨生云计算 #出海必读 #知识星球
2.  AI才是云计算的真正推手！想了解更多出海干货，赶紧关注“雨生云计算”，加入知识星球，和众多出海精英一起抱团取暖！ [文章链接] #雨生云计算 #出海必读 #知识星球
3.  出海，就要玩转云！雨生手把手教你如何利用云计算降本增效，拓展市场，提升竞争力！ [文章链接] #雨生云计算 #出海必读

---

**新闻原文中英文对照：**



Cloud computing has become so normal, it's invisible. Maybe someday we'll just call it 'data processing' again.

云计算已经变得如此普遍，它已经隐形了。也许有一天我们会再次称它为“数据处理”。

Because of the huge expense of computers in the 1960s and 1970s, mainframes and minicomputers were time-shared machines, and capacity was often rented out under a utility model. Such were the roots of cloud computing as we have come to know it. In that sense, cloud is a trip back to the future...

由于 1960 年代和 1970 年代计算机的巨额费用，大型机和小型机是分时机器，容量通常以公用事业模式出租。这就是我们所知道的云计算的根源。从这个意义上说，云计算是一次回到未来的旅行......

For example, no one talks about minicomputers anymore, even though for the better part of a decade, most of the companies in the world had minicomputers for their "data processing" and not the big, expensive behemoths known as mainframes. So, to a certain extent, most of the x86 machines sold in the past three decades qualified as "minicomputers" individually and as "clusters" when they shared workloads and as "supercomputers" when they distributed a workload across their memories either loosely (as with the MPI protocol in HPC and AI applications) or tightly on much smaller numbers of nodes.

例如，没有人再谈论小型机了，即使在过去的十年中，世界上大多数公司都使用小型机进行“数据处理”，而不是使用被称为大型机的又大又昂贵的庞然大物。因此，在某种程度上，过去三十年销售的大多数 x86 机器都符合“小型机”（单独而言）和“集群”（当它们共享工作负载时）以及“超级计算机”（当它们在内存中分布工作负载时）（无论是松散地（如 HPC 和 AI 应用程序中的 MPI 协议）还是紧密地（在数量少得多的节点上））。

Because of the huge expense of computers in the 1960s and 1970s, mainframes and minicomputers were time-shared machines, and capacity was often rented out under a utility model. Such were the roots of cloud computing as we have come to know it. In that sense, cloud is a trip back to the future, although with some important distinctions. The advent of grid computing – where compute and storage resources were lashed together on a global basis – is the second big part of the foundation of cloud computing. Cloud is the love-child of these two, really, and there was a very intentional marriage that culminated in March 2006 with the launch of Amazon Web Services.

由于 1960 年代和 1970 年代计算机的巨额费用，大型机和小型机是分时机器，容量通常以公用事业模式出租。这就是我们所知道的云计算的根源。从这个意义上说，云计算是一次回到未来的旅行，尽管有一些重要的区别。网格计算的出现——计算和存储资源在全球范围内结合在一起——是云计算基础的第二大组成部分。云实际上是这两者的爱情结晶，并且在 2006 年 3 月亚马逊网络服务 (Amazon Web Services) 的推出中达到了高潮。

It is fun to return to the original definition of cloud computing from the US Department of Commerce's National Institute of Standards and Technology, which shows how far we have come compared to those early timesharing ways four and five decades ago. To quote, the five essential characteristics of cloud computing are:

回顾美国商务部国家标准与技术研究院对云计算的最初定义是很有趣的，它表明与四五十年前的那些早期分时方式相比，我们已经走了多远。引用一下，云计算的五个基本特征是：

On-Demand Self-Service: A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.

按需自助服务：消费者可以单方面地按需自动配置计算能力，例如服务器时间和网络存储，而无需与每个服务提供商进行人工交互。

Broad Network Access: Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).

广泛的网络访问：可以通过网络访问这些功能，并通过标准机制访问这些功能，这些机制促进异构瘦客户端或胖客户端平台（例如，手机、平板电脑、笔记本电脑和工作站）的使用。

Resource Pooling: The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth.

资源池：提供商的计算资源被集中起来，以使用多租户模型为多个消费者提供服务，不同的物理和虚拟资源根据消费者需求进行动态分配和重新分配。存在位置独立性，因为客户通常无法控制或了解所提供资源的准确位置，但可能能够在更高层次的抽象级别（例如，国家、州或数据中心）指定位置。资源的示例包括存储、处理、内存和网络带宽。

Rapid Elasticity: Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.

快速弹性：可以弹性地配置和发布功能，在某些情况下是自动的，以根据需求快速向外和向内扩展。对于消费者而言，可用于配置的功能通常看起来是无限的，并且可以随时以任何数量进行分配。

Measured Service: Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

计量服务：云系统通过利用在某种程度上适合服务类型（例如，存储、处理、带宽和活动用户帐户）的计量能力来自动控制和优化资源使用。可以监视、控制和报告资源使用情况，从而为所利用服务的提供商和消费者提供透明度。

This definition of cloud implies – but does not require – either server virtualization or a higher-level container architecture or application podding system, as we have learned from the past two decades. You can have a bare metal cloud, of course. However, if you don't want to manage the underlying infrastructure, it is helpful to do what Google has done longer than anyone: virtualize and containerize compute, networking, and storage capacity across datacenters and let people schedule massive applications across that, without having to even think about the underlying infrastructure.

正如我们在过去二十年中了解到的，云的这种定义意味着——但不要求——服务器虚拟化或更高级别的容器架构或应用程序 podding 系统。当然，你可以拥有裸机云。但是，如果你不想管理底层基础设施，那么最好效仿 Google 比任何人都做得更久的做法：虚拟化和容器化跨数据中心的计算、网络和存储容量，让人们可以在其上调度大规模应用程序，而无需考虑底层基础设施。

Another transformative aspect of the cloud is the perception of infinite capacity, which of course is an illusion. Just ask anyone who is trying to buy GPU capacity on a cloud to train some AI models.

云的另一个变革性方面是对无限容量的感知，这当然是一种错觉。只需问问那些试图在云上购买 GPU 容量来训练一些 AI 模型的人。

About a decade ago, when we were touring the Microsoft Azure datacenters in Quincy, Washington, we asked Jason Zander, who was in charge of Azure infrastructure at the time, how much bandwidth was on the Azure network. And he said that he honestly didn't know, but it was so much that for all intents and purposes, even for the largest internal Microsoft use cases, that it appeared to have infinite capacity.

大约十年前，当我们参观华盛顿州昆西的 Microsoft Azure 数据中心时，我们询问了当时负责 Azure 基础设施的 Jason Zander，Azure 网络上有多少带宽。他说他真的不知道，但它太多了，以至于在所有意图和目的上，即使对于最大的 Microsoft 内部用例，它似乎也具有无限的容量。

This perception is obviously attractive because it eliminates the worry of a capacity ceiling, which plagued IT shops for many of computing's early decades. Workloads consistently outgrew annual capacity increases, giving IT pros lots of heartburn. Moreover, on-premises IT shops had to overprovision their infrastructure to deal with peaks during certain periods. The rule of thumb was to always have at least 30 percent capacity in reserve.

这种看法显然很有吸引力，因为它消除了对容量上限的担忧，这种担忧困扰了 IT 部门在计算的早期几十年中。工作负载持续超过年度容量增长，给 IT 专业人士带来了很多麻烦。此外，本地 IT 部门必须过度配置其基础设施以应对某些时期的峰值。经验法则是始终保留至少 30% 的容量。

With the advent of clouds, this overprovisioning was offloaded to the clouds themselves and that, coupled with pay-per-use pricing where you can turn capacity off, explains why IT shops are willing to pay such a high premium to rent capacity rather than install it on premises and milk the hell out of it for five, six, or seven years.

随着云的出现，这种过度配置被卸载到云本身，再加上可以关闭容量的按使用量付费的定价，解释了为什么 IT 部门愿意支付如此高的溢价来租用容量，而不是将其安装在本地并疯狂地使用五年、六年或七年。

People talked about cloud computing a lot in the 2000s and the 2010s, and it has become normal and ubiquitous here in the 2020s. But it is arguable if cloud is yet dominant, even with on-premises cloud pricing on IT gear available from Hewlett Packard Enterprise, Dell, Cisco Systems, IBM, Lenovo, and other big original equipment manufacturers serving enterprises around the world.

人们在 2000 年代和 2010 年代谈论了很多关于云计算的事情，并且在 2020 年代，它已经变得正常和普遍。但即使在 Hewlett Packard Enterprise、戴尔、思科系统、IBM、联想和其他大型原始设备制造商为全球企业提供服务的 IT 设备上提供本地云定价，云是否仍占主导地位仍值得商榷。

Nvidia details its itty bitty GB10 superchip for local AI development

英伟达详细介绍了其用于本地 AI 开发的超小 GB10 超级芯片

Qualcomm working on datacenter CPU and in ‘advanced discussions’ with hyperscaler

高通正在研发数据中心 CPU，并与超大规模企业进行“高级讨论”

Datacenter lobby blows a fuse over EU efficiency proposals

数据中心游说团体对欧盟的效率提案感到不满

More customers asking for Google's Data Boundary, says Cloud Experience boss

更多客户要求 Google 的数据边界，Cloud Experience 老板说

IDC has an interesting way of carving up aggregated compute, storage, and networking revenues by cloud and non-cloud consumption models. This particular IDC cloud infrastructure dataset takes the market researcher a long time to put together, so the latest data we have access to is for the fourth quarter of 2024 with a forecast for 2025. Here is the quarterly trend for the past decade:

IDC 有一种有趣的方式，可以根据云和非云消费模式来划分聚合的计算、存储和网络收入。IDC 的这个特定的云基础设施数据集需要市场研究人员花费很长时间才能整理出来，因此我们能够访问的最新数据是 2024 年第四季度的数据，并附带 2025 年的预测。以下是过去十年的季度趋势：

(IDC Cloud Infrastructure Chart)

This is not the value of the infrastructure as it is rented by end users, but the value of the infrastructure as it is being acquired by companies, be they using it for their own purposes or setting it up for rental by others.

这不是最终用户租用的基础设施的价值，而是公司获取的基础设施的价值，无论他们是将其用于自己的目的还是将其设置为供他人租用。

IDC breaks down the cloud market into two different types: infrastructure that is shared, as on the big clouds in most cases, and infrastructure that is dedicated to specific customers, such as on-premises utility-priced gear as well as systems sold through hosting companies or co-location facilities. The non-cloud stuff presumably includes bare metal iron (and we would love to know how much) that is unvirtualized as well as traditional legacy systems like those still sold by IBM in its Power Systems and System z lines.

IDC 将云市场分为两种不同的类型：共享的基础设施（如大多数情况下的大型云）和专用于特定客户的基础设施（如本地实用程序定价设备以及通过托管公司或主机托管设施销售的系统）。非云的东西可能包括裸机（我们很想知道有多少）以及传统的遗留系统，如 IBM 在其 Power Systems 和 System z 系列中仍在销售的那些系统。

While the growth of cloud is obvious, the tenacity of those legacy systems is also noteworthy. Some applications are very hard to replace, although we admit that AI-assisted code transformation tools are going to make it easier to break up monolithic COBOL and RPG applications and port them to cheaper cloud infrastructure. There are big benefits to staying on these IBM platforms, however, so do not expect a mass exodus, but more of a trickle exit.

虽然云的增长是显而易见的，但这些遗留系统的韧性也值得注意。有些应用程序很难替换，尽管我们承认 AI 辅助的代码转换工具将使分解单片 COBOL 和 RPG 应用程序并将其移植到更便宜的云基础设施变得更加容易。然而，留在这些 IBM 平台上有很多好处，因此不要期望出现大规模外流，而只是涓涓细流式的退出。

(IDC Cloud Infrastructure Table)

There are some interesting things to note here. First, there was a boom in shared cloud spending in 2024, thanks in large part to the GenAI boom, and this part of the IT infrastructure market continues to grow nicely in 2025 according to the forecast.

这里有一些有趣的事情需要注意。首先，2024 年共享云支出出现繁荣，这在很大程度上归功于 GenAI 的繁荣，根据预测，IT 基础设施市场的这一部分将在 2025 年继续良好增长。

But the big boom this year is for dedicated cloud capacity, which is partly explained by the neoclouds (who are financially more like hosting providers than clouds) as well as the uptake of utility-priced IT gear from the OEMs. We can't say for sure on the latter data because IDC stopped talking about "dedicated on premises" gear as opposed to "dedicated on cloud" gear back in 2023.

但今年最大的繁荣是专用于云容量，这部分归功于 neoclouds（他们在财务上更像托管提供商而不是云），以及原始设备制造商对实用程序定价 IT 设备的采用。我们无法确定后一种数据，因为 IDC 在 2023 年停止谈论“本地专用”设备而不是“云专用”设备。

The forecast out through 2029, you will also note, shows dedicated cloud growing faster than shared cloud, again thanks to the GenAI boom and the rise of the neoclouds. But shared cloud will nonetheless be the main use case for systems acquired. It also looks like IDC expects very healthy growth in IT infrastructure over the next six years, with it nearly doubling from $275.3 billion in 2024 to $556.3 billion in 2029, inclusive.

你还会注意到，到 2029 年的预测表明，专用于云的增长速度快于共享云，这再次归功于 GenAI 的繁荣和 neoclouds 的兴起。但共享云仍然将是系统获取的主要用例。IDC 似乎还预计未来六年 IT 基础设施将实现非常健康的增长，从 2024 年的 2753 亿美元几乎翻一番，到 2029 年达到 5563 亿美元（含）。

At some point, "cloud computing" will just be "computing." Or maybe, reaching back further in time, we will call it "data processing" as the nerds from the 1960s did. ®

在某个时候，“云计算”将仅仅是“计算”。或者，追溯到更早的时候，我们将像 1960 年代的书呆子那样称它为“数据处理”。®
https://share.google/tnPP4hopdCkSbXMMk**记得关注“雨生云计算”公众号，带你解锁更多出海干货！**

雨生的小红书

雨生课程