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各位出海的弄潮儿，新年好呀！今天是2026年的第一天。
外面的世界风起云涌，AI 浪潮惊涛拍岸，但咱们出海人从来不怕浪大，就怕水不活！新的一年，怎么在云端“骑龙”而不是“被龙骑”？怎么让每一分算力都变成实打实的利润？
雨生这里有三个压箱底的锦囊，祝大家2026年—— 云稳、算准、赚得狠！
🧧 2026开工大吉：送你三个“云端锦囊”，助你算力如龙，财源滚滚！
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兄弟们，过去咱们讲“云优先（Cloud-First）”，那是为了快，为了赶时髦。但到了2026年，咱们要讲究一个 “灵” 字！
现在的AI技术这么强，算力这么贵，咱们不能再当“傻大款”了。德勤（Deloitte）最新的报告都说了，纯云架构跑AI，那就是在碎钞票。
新年的第一个发财秘籍就是： 做个精明的“云端买手”！
训练去云端，大展宏图： 需要爆发力的时候，咱就用公有云的无限弹药，那是咱们的“倚天剑”，随时扩容，随时开战！
推理回本地，稳坐钓鱼台： 那些天天跑、月月跑的稳定业务，咱不妨请回本地或者边缘端。这就好比把租来的豪车换成了自家的千里马，**骑着舒服，养着便宜！
这一招使出来，保证你的云账单“瘦身”成功，企业的利润率“增肌”猛涨！把省下来的真金白银，发给兄弟们当开工利是，它不香吗？
咱们出海做生意，讲究的是广结善缘，四海通吃。在云上也一样！
2026年，大卫·林西库姆（David Linthicum）预言这将是“不再信任单一云”的一年。咱们别把身家性命绑在一棵树上。咱们要 “狡兔三窟” ，还要 “左右逢源” ！
AWS 是好哥们，Azure 是好兄弟，Google Cloud 也是好伙伴。哪家服务好、哪家不宕机、哪家价格优，咱们就用哪家！
业务不单吊： 核心系统搞个“双活”甚至“多活”。这边有个风吹草动，那边立马无缝衔接。
数据要握紧： 关键数据这颗“龙珠”，一定要有自己的掌控权，这就是咱们出海的底气。
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——从“API接口”进化为“超级操盘手”，你就是时代的C位！
新的一年，AI 不会淘汰人，但 “会用AI的人”会淘汰“不会用的人” ，更重要的是， “有思想的人”将统领“有算力的人”！
拒绝机械忙碌： 把那些枯燥的、重复的活儿，统统交给AI去干。
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2026年，不做算力的“耗材”，要做智慧的“帅才”！让AI成为你脚下的风火轮，助你一日千里，马到成功！
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📰 新闻原文对照 (中英双语)
**Article 1: 2026: The year we stop trusting any single cloud**
**文章一：2026：我们不再信任单一云的一年**
The next wave of cloud transformation will be about strategic dependence, resilience, and architectural honesty.
下一波云转型浪潮将围绕战略依赖性、韧性和架构诚实性展开。
For more than a decade, many considered cloud outages a theoretical risk, something to address on a whiteboard and then quietly deprioritize during cost cuts. In 2025, this risk became real. A major Google Cloud outage in June caused hours-long disruptions to popular consumer and enterprise services, with ripple effects into providers that depend on Google’s infrastructure. Microsoft 365 and Outlook also faced code failures and notable outages, as did collaboration platforms like Slack and Zoom. Even security platforms and enterprise backbones suffered extended downtime.
十多年来，许多人认为云中断只是理论上的风险，是在白板上讨论然后在削减成本时悄悄降低优先级的议题。但在 2025 年，这一风险变成了现实。6 月份谷歌云的一次重大中断导致流行的消费者和企业服务中断数小时，并波及依赖谷歌基础设施的供应商。Microsoft 365 和 Outlook 也面临代码故障和严重中断，Slack 和 Zoom 等协作平台也是如此。甚至安全平台和企业骨干网也遭受了长时间的停机。
None of these incidents, individually, was apocalyptic. Collectively, they changed the tone in the boardroom. Executives who once saw cloud resilience as an IT talking point suddenly realized that a configuration change in someone else’s platform could derail support queues, warehouse operations, and customer interactions in one stroke.
这些事件单独来看都不是世界末日。但总的来说，它们改变了董事会的基调。曾经将云韧性视为 IT 话题的高管们突然意识到，别人平台上的配置更改可能会瞬间让支持队列、仓库运营和客户互动脱轨。
Relying on one provider is risky
The real story is not that cloud platforms failed; it’s that enterprises quietly allowed those platforms to become single points of failure for entire business models. In 2025, many organizations discovered that their digital transformation had traded physical single points of failure for logical ones in the form of a single region, a single provider, or even a single managed database. When a hyperscaler region had trouble, companies learned the hard way that “highly available within a region” is not the same as “business resilient.”
真正的故事不是云平台失败了；而是企业悄悄地允许这些平台成为整个商业模式的单点故障。在 2025 年，许多组织发现他们的数字化转型用单一区域、单一供应商甚至单一托管数据库形式的逻辑单点故障，交换了物理单点故障。当超大规模云服务商的某个区域出现问题时，公司痛苦地学到了“区域内高可用”并不等同于“业务韧性”。
What caught even seasoned teams off guard was the hidden dependency chain. Organizations that thought they were cloud-agnostic because they used a SaaS provider discovered that the SaaS itself was entirely dependent on a single cloud region. When that region faltered, so did the SaaS—and by extension, the business. This is why 2026 will be the year where dependence itself, not just uptime numbers, becomes a primary design concern.
让即使是经验丰富的团队也措手不及的是隐藏的依赖链。那些以为因为使用了 SaaS 提供商就是云中立的组织发现，SaaS 本身完全依赖于单一的云区域。当该区域动摇时，SaaS 也会动摇——进而波及业务。这就是为什么 2026 年将是依赖性本身（而不仅仅是正常运行时间数字）成为主要设计关注点的一年。
Resilience gets its own budget line
Every downturn and major outage reshapes budgets. The 2025 incidents are doing that right now. I’m seeing CIOs and CFOs move away from the idea that resilience is something you squeeze in if there’s leftover budget after cost optimization. Instead, resilience is getting explicit funding, with line items for multiregion architectures, modernized backup and restore, and cross-cloud or hybrid continuity strategies.
每一次低迷和重大中断都会重塑预算。2025 年的事件正在做到这一点。我看到 CIO 和 CFO 正在摒弃这样一种观念：如果成本优化后还有剩余预算，再把韧性塞进去。相反，韧性正在获得明确的资金支持，包括多区域架构、现代化备份和恢复以及跨云或混合连续性策略的预算项目。
This is a shift in mindset as much as in money. We once justified resilience in terms of compliance or technical best practices. In 2026, we’ll look for direct revenue protection and risk reduction, often backed by concrete numbers from the 2025 outages: lost transactions, missed service-level agreements, overtime for remediation, and reputational damage. Once those numbers are quantified, resilience stops being a nice to have and becomes a board-sanctioned business control.
这既是心态的转变，也是资金的转变。我们曾经用合规性或技术最佳实践来证明韧性的合理性。在 2026 年，我们将寻求直接的收入保护和风险降低，通常由 2025 年中断的具体数据支持：丢失的交易、错过的服务水平协议、补救加班费和声誉受损。一旦这些数字被量化，韧性就不再是可有可无的东西，而变成了董事会批准的业务控制。
For years, enterprises talked about cloud portability and avoiding lock-in. They then deeply embedded themselves in proprietary services for speed and convenience. 2026 is when many of those same organizations will take a second look and start moving selected workloads and data into more portable, resilient architectures. That does not mean a mass exodus from the major clouds; it means being far more deliberate about which workloads live where and why.
多年来，企业一直在谈论云可移植性和避免锁定。然后为了速度和便利，他们深深地嵌入了专有服务中。2026 年，许多这些组织将重新审视并开始将选定的工作负载和数据转移到更便携、更有弹性的架构中。这并不意味着大规模撤离主要云平台；这意味着对哪些工作负载放在哪里以及为什么放在那里要更加深思熟虑。
Expect to see targeted workload shifts that move critical customer-facing systems from single-region to multi-region or cross-cloud setups, re-architecting data platforms with replicated storage and active-active databases (meaning that we have two running, with one backing up the other). Also, relocating some systems to private or colocation environments based on risk. Systems that could significantly halt revenue or operations will have their placement and dependencies reassessed.
预计会看到有针对性的工作负载转移，将面向关键客户的系统从单一区域转移到多区域或跨云设置，重构具有复制存储和双活数据库（意味着我们有两个在运行，一个备份另一个）的数据平台。此外，根据风险将某些系统重新安置到私有或托管环境中。可能显著停止收入或运营的系统将重新评估其位置和依赖关系。
Redundancy stops being a luxury
In the early cloud days, active-active architectures across regions—or worse, across providers—were viewed as exotic and expensive. In 2026, for selected tiers of applications and data, they will be considered baseline engineering hygiene. The outages of 2025 demonstrated that running “hot–warm” with manual failover often means you are functionally down for hours when you can least afford it.
在云的早期，跨区域——或者更糟糕的是跨供应商——的双活架构被视为奇特且昂贵的。在 2026 年，对于选定的应用程序和数据层级，它们将被视为基准工程卫生。2025 年的中断表明，运行带有手动故障转移的“热-温”模式通常意味着你在最无法承受的时候实际上会停机数小时。
The response will include more active-active patterns: stateless services across regions managed globally, multi-region data stores with conflict resolution, and messaging layers resilient to provider issues. Enterprises will adopt chaos engineering and failure testing as ongoing practices, requiring continuous resilience proof beyond disaster recovery records.
应对措施将包括更多的主动-主动模式：全球管理的跨区域无状态服务、具有冲突解决功能的多区域数据存储以及对提供商问题具有弹性的消息传递层。企业将采用混沌工程和故障测试作为持续的实践，要求除了灾难恢复记录之外的持续弹性证明。
Rethinking third-party services
One of the more uncomfortable lessons from 2025 was that indirect cloud dependence can hurt just as much as direct dependence. Several SaaS and platform providers marketed themselves as simplifying complexity and insulating customers from cloud details, yet internally ran everything in a single cloud, sometimes a single region. When their underlying cloud experienced issues, customers found they had no visibility, no leverage, and no alternative.
2025 年更令人不舒服的教训之一是，间接云依赖的危害与直接依赖一样大。几家 SaaS 和平台提供商推销自己可以简化复杂性并让客户免受云细节的影响，但在内部却都在单一云（有时甚至是单一区域）中运行一切。当他们的底层云遇到问题时，客户发现自己没有可见性、没有筹码，也没有替代方案。
In 2026, smart enterprises will start asking their vendors the hard questions. Which regions and providers do you use? Do you have a tested failover strategy across regions or providers? What happens to my data and SLAs if your primary cloud has a regional incident? Many will diversify not just across hyperscalers, but across SaaS and managed services, deliberately avoiding over-concentration on any provider that cannot demonstrate meaningful redundancy.
2026 年，明智的企业将开始向供应商提出棘手的问题。你们使用哪些区域和提供商？你们是否有经过测试的跨区域或提供商故障转移策略？如果你的一级云发生区域性事件，我的数据和 SLA 会怎样？许多企业不仅会在超大规模企业之间实现多元化，而且会在 SaaS 和托管服务之间实现多元化，刻意避免过度集中于任何无法证明有意义冗余的提供商。
Embracing resilience in 2026
If 2025 was the wake-up call, 2026 will be the year to act with discipline. That starts with an honest dependency inventory: not just which clouds you use directly, but which clouds and regions sit beneath your SaaS, security, networking, and operations tools. From there, you can classify systems by business criticality and map appropriate resilience patterns to each class, reserving the most expensive mechanisms, such as cross-region active-active, for systems where downtime is truly existential.
如果 2025 年是警钟，那么 2026 年将是采取纪律行动的一年。这始于诚实的依赖性清单：不仅是你直接使用哪些云，还有哪些云和区域位于你的 SaaS、安全、网络和运营工具之下。从那里，你可以按业务关键性对系统进行分类，并将适当的弹性模式映射到每个类别，为停机真正关乎生死的系统保留最昂贵的机制（例如跨区域双活）。
Equally important is organizational change. Resilience is not only an architectural problem; it is an operations, finance, and governance problem. In 2026, the enterprises that succeed will be the ones that align architecture, site reliability engineering, security, and finance around a shared goal: reduce single points of failure in both technology and vendors, validate failover and recovery as rigorously as new features, and treat cloud dependence as a managed business risk rather than a hidden assumption. The cloud is not going away, nor should it, but our blind trust in any single piece of it must stop.
同样重要的是组织变革。韧性不仅仅是一个架构问题；这是一个运营、财务和治理问题。在 2026 年，成功的企业将是那些围绕共同目标调整架构、站点可靠性工程、安全和财务的企业：减少技术和供应商中的单点故障，像验证新功能一样严格验证故障转移和恢复，并将云依赖视为一种受管业务风险，而不是一种隐藏的假设。云不会消失，也不应该消失，但我们对其中任何一部分的盲目信任必须停止。
Article Link: https://www.infoworld.com/article/3635398/2026-the-year-we-stop-trusting-any-single-cloud.html
**Article 2: AI killed the cloud-first strategy: Why hybrid computing is the only way forward now**
**文章二：AI 杀死了云优先战略：为什么混合计算现在是唯一的出路**
Five years ago, cloud was the answer to everything. With AI, that's no longer so clear.
五年前，云是一切的答案。有了 AI，这就不再那么清楚了。
A decade or so ago, the debate between cloud and on-premises computing raged. The cloud handily won that battle, and it wasn't even close. Now, however, people are rethinking whether the cloud is still their best choice for many situations.
大约十年前，云计算和本地计算之间的争论非常激烈。云轻松赢得了那场战斗，而且优势明显。然而现在，人们正在重新思考云在许多情况下是否仍然是他们的最佳选择。
Welcome to the age of AI, in which on-premises computing is starting to look good again.
欢迎来到 AI 时代，在这个时代，本地计算开始再次看起来不错。
Existing infrastructures now configured with cloud services simply may not be ready for emerging AI demands, a recent analysis from Deloitte warned.
德勤最近的一份分析警告说，目前配置了云服务的现有基础设施可能根本无法满足新兴的 AI 需求。
"The infrastructure built for cloud-first strategies can't handle AI economics," the report, penned by a team of Deloitte analysts led by Nicholas Merizzi, said.
“为云优先战略构建的基础设施无法处理 AI 经济学，”这份由 Nicholas Merizzi 领导的德勤分析师团队撰写的报告称。
"Processes designed for human workers don't work for agents. Security models built for perimeter defense don't protect against threats operating at machine speed. IT operating models built for service delivery don't drive business transformation."
“为人类工人设计的流程不适用于智能体。为外围防御建立的安全模型无法防御机器速度运行的威胁。为服务交付建立的 IT 运营模型无法推动业务转型。”
To meet the needs of AI, enterprises are contemplating a shift away from mainly cloud to a hybrid mix of cloud and on-premises, according to the Deloitte analysts. Technology decision-makers are taking a second and third look at on-premises options.
根据德勤分析师的说法，为了满足 AI 的需求，企业正在考虑从主要依赖云转向云和本地的混合组合。技术决策者正在对本地选项进行第二次和第三次审视。
As the Deloitte team described it, there's a movement afoot "from cloud-first to strategic hybrid -- cloud for elasticity, on-premises for consistency, and edge for immediacy."
正如德勤团队所描述的那样，一场“从云优先到战略混合——云用于弹性，本地用于一致性，边缘用于即时性”的运动正在进行中。
The Deloitte analysts cited four burning issues that are arising with cloud-based AI:
德勤分析师列举了基于云的 AI 出现的四个紧迫问题：
Rising and unanticipated cloud costs: AI token costs have dropped 280-fold in two years, they observe -- yet "some enterprises are seeing monthly bills in the tens of millions." The overuse of cloud-based AI services "can lead to frequent API hits and escalating costs." There's even a tipping point in which on-premises deployments make more sense. "This may happen when cloud costs begin to exceed 60% to 70% of the total cost of acquiring equivalent on-premises systems, making capital investment more attractive than operational expenses for predictable AI workloads."
不断上升且未预料到的云成本：他们观察到，AI token 成本在两年内下降了 280 倍——然而“一些企业每月的账单却高达数千万”。过度使用基于云的 AI 服务“可能导致频繁的 API 点击和成本不断攀升”。甚至存在一个转折点，此时本地部署更有意义。“当云成本开始超过获得同等本地系统总成本的 60% 到 70% 时，这种情况可能会发生，对于可预测的 AI 工作负载，资本投资比运营支出更具吸引力。”
Latency issues with cloud: AI often demands near-zero latency to deliver actions. "Applications requiring response times of 10 milliseconds or below cannot tolerate the inherent delays of cloud-based processing," the Deloitte authors point out.
云的延迟问题：AI 通常需要近乎零的延迟来交付动作。“要求响应时间在 10 毫秒或以下的应用程序无法容忍基于云的处理的固有延迟，”德勤作者指出。
On-premises promises greater resiliency: Resilience is also part of the pressing requirements for fully functional AI processes. These include "mission-critical tasks that cannot be interrupted require on-premises infrastructure in case connection to the cloud is interrupted," the analysts state.
本地部署承诺更大的韧性：韧性也是功能齐全的 AI 流程的迫切要求之一。分析师指出，这些包括“不能中断的关键任务任务需要本地基础设施，以防连接到云中断。”
Data sovereignty: Some enterprises "are repatriating their computing services, not wanting to depend entirely on service providers outside their local jurisdiction."
数据主权：一些企业“正在将计算服务遣返，不想完全依赖于本地司法管辖区以外的服务提供商。”
The best solution to the cloud versus on-premises dilemma is to go with both, the Deloitte team said. They recommend a three-tier approach, which consists of the following:
德勤团队表示，解决云与本地困境的最佳方案是两者兼顾。他们建议采用三层方法，包括：
Cloud for elasticity: To handle variable training workloads, burst capacity needs, and experimentation.
云用于弹性：处理可变的训练工作负载、突发容量需求和实验。
On-premises for consistency: Run production inference at predictable costs for high-volume, continuous workloads.
本地用于一致性：以可预测的成本运行生产推理，用于大容量、连续的工作负载。
Edge for immediacy: This means AI within edge devices, apps, or systems that handle "time-critical decisions with minimal latency, particularly for manufacturing and autonomous systems where split-second response times determine operational success or failure."
边缘用于即时性：这意味着边缘设备、应用程序或系统内的 AI 处理“具有最小延迟的时间关键决策，特别是对于制造和自主系统，在这些系统中，瞬间的响应时间决定了运营的成败。”
This hybrid approach resonates as the best path forward for many enterprises. Milankumar Rana, who recently served as software architect at FedEx Services, is all-in with cloud for AI, but sees the need to support both approaches where appropriate.
这种混合方法作为许多企业的最佳前进路径产生了共鸣。最近担任 FedEx Services 软件架构师的 Milankumar Rana 全力支持云 AI，但也看到需要在适当的地方支持这两种方法。
"I have built large-scale machine learning and analytics infrastructures, and I have observed that almost all functionalities, such as data lakes, distributed pipelines, streaming analytics, and AI workloads based on GPUs and TPUs, can now run in the cloud," he told ZDNET. "Because AWS, Azure, and GCP services are so mature, businesses may grow fast without having to spend a lot of money up front."
他告诉 ZDNET：“我已经构建了大规模的机器学习和分析基础设施，我观察到几乎所有的功能，如数据湖、分布式管道、流分析以及基于 GPU 和 TPU 的 AI 工作负载，现在都可以在云中运行。”“因为 AWS、Azure 和 GCP 服务非常成熟，企业可以快速增长，而无需预先花费大量资金。”
Rana also tells customers "to maintain some workloads on-premises where data sovereignty, regulatory considerations, or very low latency make the cloud less useful," he said. "The best way to do things right now is to use a hybrid strategy, where you keep sensitive or latency-sensitive applications on-premises while using the cloud for flexibility and new ideas."
Rana 还告诉客户“将一些工作负载保留在本地，在这些地方数据主权、监管考虑或非常低的延迟使云不太有用，”他说。“现在做事的最好方法是使用混合策略，在本地保留敏感或对延迟敏感的应用程序，同时利用云来实现灵活性和新想法。”
Whether employing cloud or on-premises systems, companies should always take direct responsibility for security and monitoring, Rana said. "Security and compliance remain the responsibility of all individuals. Cloud platforms include robust security; but, you must ensure adherence to regulations for encryption, access, and monitoring."
Rana 说，无论是采用云系统还是本地系统，公司都应始终对安全和监控承担直接责任。“安全和合规仍然是所有人的责任。云平台包括强大的安全性；但是，你必须确保遵守加密、访问和监控的规定。”
Article Link: https://www.zdnet.com/article/ai-killed-the-cloud-first-strategy-why-hybrid-computing-is-the-only-way-forward-now/
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