我的征尘是星辰大海。。。
The dirt and dust from my pilgrimage forms oceans of stars...
-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆
作者:黄教授
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AI时代云战略重构
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原始脚本
AI 时代，云战略重构，从传统三分天下到新赛道博弈。 云计算发展至今， AWS Azure Google Cloud 早已形成截然不同的战略底色。 这并非短期市场策略，而是三家底层基因决定的长期路线。 而大模型 AI 的全面落地，彻底改变了企业数据形态、应用逻辑与算力需求。 既让传统云战略的优势得以放大，也倒逼厂商做出针对性迭代。 并非全盘推翻，而是惯性延续加 AI 适配的双重逻辑。 一、传统云时代，三大厂商的战略底色。 老话题复盘，传统云计算的核心是算力交付与软件上云，三家厂商走出了三条完全割裂的路线。 也奠定了当下的市场格局。 一、 aws 硬件及服务。 乐高式算力批发，彻底聚焦底层基础设施，把 cpu 存储、网络服务器拆成标准化组件，像乐高积木一样开放给企业自主拼接，不触碰上层软件与业务，主打低价、稳定、中立、开放。 不抢企业的软件生意，不做业务绑定，任何系统软件都能在 AWS 上部署。 凭借先发优势与极致规模化，牢牢占据云市场头把交椅。 是最纯粹的算力提供商。 二， Azure 企业软件一体化中间路线最优解，依托微软本身的企业软件霸权，将 Office Windows SQL Server Active Directory 等企业刚需软件从本地买断制转为云端订阅制，走 PaaS 加 SaaS 混搭路线。 不做纯硬件批发，也不强行替代企业现有软件，而是把企业已在用的工具无缝搬上云，兼顾私有云、混合云部署。 完美适配企业现有 IT 架构、权限体系与运维习惯，是企业上云最省心的选择，赢在企业生态兼容性。 三、 Google Cloud 服务化架构定义云端新服务，野心最大，彻底跳出硬件思维，认为企业需要的不是服务器与组件，而是直接可用的云端服务。 从 Google Docs Cloud SQL 到 BigQuery 从零打造云端全链路服务，试图替代企业传统软件，赚软件开发与服务的钱。 其架构基于自研的 Borg Omega，极致适配互联网规模与分布式场景，但过于互联网原生，与企业传统 IT 体系脱节，落地难度极高。 始终位居第三。 二、 ai 时代到来，彻底颠覆云服务的核心逻辑。 大模型 ai 的普及不是云计算的简单升级。 而是从底层改变了企业对云的需求。 核心变革集中在三点一，数据形态彻底转变，企业核心数据不再只是数据库里的结构化字段。 视频、语音、图片、用户行为日志等非结构化数据成为主流。 传统关系型数据库，如 Oracle 无法承载查询与分析，依赖 AI 大模型做内容理解。 特征提取，而非传统的字段检索。 二、应用逻辑从流程化走向数据驱动。 以往企业应用是固定流程、固定算法。 靠程序员写死代码实现。 AI 时代的推荐、内容识别、用户画像，需要模型实时学习数据，自动迭代，靠数据驱动，而非人工改代码。 对算力弹性、模型调度、显存管理的需求远超传统云服务。 三、算力需求从通用走向专属 AI 推理。 训练需要海量 GPU 算力，且存在明显的波峰波谷，对 GPU 虚拟化、显存池化、弹性扩缩容的要求极高，传统通用算力无法满足。 云厂商的核心竞争力从卖算力转向 AI 算力调度与模型运维。 三、 AI 时代三大云厂商战略延续与新迭代。 三家厂商并未抛弃原有优势。 而是沿着自身基因叠加 AI 能力做战略升级，路线差异愈发清晰。 一、 AWS 坚守硬件优势。 做 AI 时代的中立算力底座，延续乐高式开放理念，不自研专属大模型，而是打造 AI 算力枢纽，集成 Claude llama GPTOSS 等全球主流模型，让企业自主选择、自由组合，加大 AI 芯片与专属数据中心投入，优化训练与推理成本。 依旧主打中立开放，不与企业抢 AI 应用市场，让所有模型厂商、企业 AI 应用都能依托其底层算力落地，靠极致规模化守住市场份额。 核心是做 AI 时代的水电煤，不站队，不绑定。 二、 Asher 依托企业生态，做 AI 与企业软件的深度融合，延续企业软件一体化优势。 将 OpenAI Anthropic 模型与 Office GitHub VS Code 企业数据库全面打通，把 AI 能力嵌入企业已在用的全流程工具中。 企业无需改变现有 it 架构，就能在办公、开发、运维中直接调用 AI 同时凭借全球云架构与路由模型，实现模型按需调度。 成本极致压缩，如10美元 copilot 核心是降低企业 ai 落地门槛，让 ai 成为企业软件的标配，牢牢锁住存量企业客户。 靠生态粘性实现增长。 三、 Google Cloud 强化服务化基因，做全云端 AI 原生架构延续服务定义一切的路线。 依托 gemini 与自研架构，打造云端 ai 开发加部署全闭环，推进浏览器端、云端开发，彻底摆脱本地工具依赖。 针对 ai 非结构化数据场景。 优化大数据与 AI 模型的协同能力，适配视频、语音等内容理解与推荐场景。 同时发力混合云与边缘计算，试图弥补企业适配性短板。 其路线依旧是最前瞻的。 瞄准未来全云端 AI 原生场景，只是短期落地仍受企业传统架构制约，长期价值需时间兑现。 四、 AI 时代云战略的核心启示一。 没有最优战略，只有适配基因的战略。 三家厂商均未推翻原有路线，而是放大优势适配 AI 证明云战略的核心是基因延续。 而非盲目跟风。 二、企业痛点始终是核心。 Air 的成功本质是贴合企业不愿重构 it 只想平滑升级的痛点。 AWS 赢在中立，满足企业多元化需求。 Google 的难点在于过于超前，与企业现有体系脱节。 三。 AI 不再是云的附加功能，而是核心引擎。 云厂商的竞争从传统算力价格、稳定性转向 AI 算力调度。 模型运维生态整合，模型本身的差异缩小，工程化能力成为决胜关键。 四、传统软件厂商的教训。 Oracle 因固守本地数据库。 抵触云转型陷入困境，印证了不顺应时代技术趋势，再强的用户粘性也会被颠覆。 AI 时代企业服务的核心是适配新的数据形态与应用逻辑。
修正脚本
AI 时代，云战略重构，从传统三分天下到新赛道博弈。 云计算发展至今， AWS Azure Google Cloud 早已形成截然不同的战略底色。 这并非短期市场策略，而是三家底层基因决定的长期路线。 而大模型 AI 的全面落地，彻底改变了企业数据形态、应用逻辑与算力需求。 既让传统云战略的优势得以放大，也倒逼厂商做出针对性迭代。 并非全盘推翻，而是惯性延续加 AI 适配的双重逻辑。 一、传统云时代，三大厂商的战略底色。 老话题复盘，传统云计算的核心是算力交付与软件上云，三家厂商走出了三条完全割裂的路线。 也奠定了当下的市场格局。 一、 AWS 硬件及服务。 乐高式算力批发，彻底聚焦底层基础设施，把 CPU 存储、网络服务器拆成标准化组件，像乐高积木一样开放给企业自主拼接，不触碰上层软件与业务，主打低价、稳定、中立、开放。 不抢企业的软件生意，不做业务绑定，任何系统软件都能在 AWS 上部署。 凭借先发优势与极致规模化，牢牢占据云市场头把交椅。 是最纯粹的算力提供商。 二、 Azure 企业软件一体化中间路线最优解，依托微软本身的企业软件霸权，将 Office Windows SQL Server Active Directory 等企业刚需软件从本地买断制转为云端订阅制，走 PaaS 加 SaaS 混搭路线。 不做纯硬件批发，也不强行替代企业现有软件，而是把企业已在用的工具无缝搬上云，兼顾私有云、混合云部署。 完美适配企业现有 IT 架构、权限体系与运维习惯，是企业上云最省心的选择，赢在企业生态兼容性。 三、 Google Cloud 服务化架构定义云端新服务，野心最大，彻底跳出硬件思维，认为企业需要的不是服务器与组件，而是直接可用的云端服务。 从 Google Docs Cloud SQL 到 BigQuery 从零打造云端全链路服务，试图替代企业传统软件，赚软件开发与服务的钱。 其架构基于自研的 Borg Omega，极致适配互联网规模与分布式场景，但过于互联网原生，与企业传统 IT 体系脱节，落地难度极高。 始终位居第三。 二、 AI 时代到来，彻底颠覆云服务的核心逻辑。 大模型 AI 的普及不是云计算的简单升级。 而是从底层改变了企业对云的需求。 核心变革集中在三点：一、数据形态彻底转变，企业核心数据不再只是数据库里的结构化字段。 视频、语音、图片、用户行为日志等非结构化数据成为主流。 传统关系型数据库，如 Oracle 无法承载查询与分析，依赖 AI 大模型做内容理解、特征提取，而非传统的字段检索。 二、应用逻辑从流程化走向数据驱动。 以往企业应用是固定流程、固定算法。 靠程序员写死代码实现。 AI 时代的推荐、内容识别、用户画像，需要模型实时学习数据，自动迭代，靠数据驱动，而非人工改代码。 对算力弹性、模型调度、显存管理的需求远超传统云服务。 三、算力需求从通用走向专属 AI 推理。 训练需要海量 GPU 算力，且存在明显的波峰波谷，对 GPU 虚拟化、显存池化、弹性扩缩容的要求极高，传统通用算力无法满足。 云厂商的核心竞争力从卖算力转向 AI 算力调度与模型运维。 三、 AI 时代三大云厂商战略延续与新迭代。 三家厂商并未抛弃原有优势。 而是沿着自身基因叠加 AI 能力做战略升级，路线差异愈发清晰。 一、 AWS 坚守硬件优势。 做 AI 时代的中立算力底座，延续乐高式开放理念，不自研专属大模型，而是打造 AI 算力枢纽，集成 Claude llama GPTOSS 等全球主流模型，让企业自主选择、自由组合，加大 AI 芯片与专属数据中心投入，优化训练与推理成本。 依旧主打中立开放，不与企业抢 AI 应用市场，让所有模型厂商、企业 AI 应用都能依托其底层算力落地，靠极致规模化守住市场份额。 核心是做 AI 时代的水电煤，不站队，不绑定。 二、 Azure 依托企业生态，做 AI 与企业软件的深度融合，延续企业软件一体化优势。 将 OpenAI Anthropic 模型与 Office GitHub VS Code 企业数据库全面打通，把 AI 能力嵌入企业已在用的全流程工具中。 企业无需改变现有 IT 架构，就能在办公、开发、运维中直接调用 AI 同时凭借全球云架构与路由模型，实现模型按需调度。 成本极致压缩，如10美元 copilot 核心是降低企业 AI 落地门槛，让 AI 成为企业软件的标配，牢牢锁住存量企业客户。 靠生态粘性实现增长。 三、 Google Cloud 强化服务化基因，做全云端 AI 原生架构延续服务定义一切的路线。 依托 gemini 与自研架构，打造云端 AI 开发加部署全闭环，推进浏览器端、云端开发，彻底摆脱本地工具依赖。 针对 AI 非结构化数据场景，优化大数据与 AI 模型的协同能力，适配视频、语音等内容理解与推荐场景。 同时发力混合云与边缘计算，试图弥补企业适配性短板。 其路线依旧是最前瞻的。 瞄准未来全云端 AI 原生场景，只是短期落地仍受企业传统架构制约，长期价值需时间兑现。 四、 AI 时代云战略的核心启示：一、 没有最优战略，只有适配基因的战略。 三家厂商均未推翻原有路线，而是放大优势适配 AI 证明云战略的核心是基因延续。 而非盲目跟风。 二、企业痛点始终是核心。 Azure 的成功本质是贴合企业不愿重构 IT 只想平滑升级的痛点。 AWS 赢在中立，满足企业多元化需求。 Google 的难点在于过于超前，与企业现有体系脱节。 三、 AI 不再是云的附加功能，而是核心引擎。 云厂商的竞争从传统算力价格、稳定性转向 AI 算力调度。 模型运维生态整合，模型本身的差异缩小，工程化能力成为决胜关键。 四、传统软件厂商的教训。 Oracle 因固守本地数据库。 抵触云转型陷入困境，印证了不顺应时代技术趋势，再强的用户粘性也会被颠覆。 AI 时代企业服务的核心是适配新的数据形态与应用逻辑。
英文翻译
In the AI era, cloud strategy is being restructured, shifting from the traditional three-way split to competition in new tracks. Since the development of cloud computing, AWS, Azure, and Google Cloud have already formed distinctly different strategic foundations. This is not a short-term market strategy but a long-term course determined by the underlying DNA of the three companies. The comprehensive deployment of large model AI has radically transformed enterprise data forms, application logic, and computing power demands. It not only amplifies the advantages of traditional cloud strategies but also forces vendors to make targeted iterations. It is not a complete overhaul but a dual logic of inertia continuation and AI adaptation. I. In the traditional cloud era, the strategic foundations of the three major vendors. Reviewing old topics, the core of traditional cloud computing is computing power delivery and software migration to the cloud. The three vendors have taken three completely divergent paths. This also established the current market landscape. I. AWS: Hardware and services. LEGO-style computing power wholesale, fully focused on underlying infrastructure, breaking CPU, storage, and network servers into standardized components, open to enterprises for self-assembly like LEGO bricks, without touching upper-layer software or business. It emphasizes low cost, stability, neutrality, and openness. It does not compete with enterprises in software business, does not lock in business, and any system software can be deployed on AWS. Leveraging first-mover advantage and extreme scale, it firmly holds the top position in the cloud market. It is the purest computing power provider. II. Azure: The optimal middle path of enterprise software integration. Relying on Microsoft's own enterprise software dominance, it converts essential enterprise software such as Office, Windows, SQL Server, and Active Directory from on-premises perpetual licenses to cloud subscription models, following a PaaS plus SaaS hybrid approach. It does not purely wholesale hardware, nor does it forcibly replace existing enterprise software. Instead, it seamlessly migrates tools already in use by enterprises to the cloud, while supporting private and hybrid cloud deployment. Perfectly adapting to existing enterprise IT architectures, permission systems, and operational habits, it is the most hassle-free choice for enterprise cloud migration, winning through enterprise ecosystem compatibility. III. Google Cloud: Service-oriented architecture defining new cloud services. With the greatest ambition, it completely breaks away from hardware thinking, arguing that what enterprises need are not servers and components but directly usable cloud services. From Google Docs, Cloud SQL to BigQuery, it builds end-to-end cloud services from scratch, attempting to replace traditional enterprise software and earning revenue from software development and services. Its architecture is based on its self-developed Borg and Omega, perfectly adapted to internet-scale and distributed scenarios, but it is too internet-native, disconnected from traditional enterprise IT systems, making deployment extremely challenging. It has always been in third place. II. The arrival of the AI era has completely overturned the core logic of cloud services. The popularization of large model AI is not a simple upgrade of cloud computing. It fundamentally changes enterprise demand for the cloud. The core changes are concentrated in three points: I. Data forms have completely transformed. Enterprise core data is no longer just structured fields in databases. Unstructured data such as video, voice, images, and user behavior logs have become mainstream. Traditional relational databases, like Oracle, cannot handle query and analysis, relying on AI large models for content understanding and feature extraction rather than traditional field retrieval. II. Application logic has shifted from process-oriented to data-driven. Previously, enterprise applications had fixed processes and fixed algorithms. They were implemented by programmers writing static code. AI-era recommendations, content recognition, and user profiling require models to learn from data in real time and iterate automatically, driven by data rather than manual code changes. The demands for computing elasticity, model scheduling, and memory management far exceed traditional cloud services. III. Computing power needs have shifted from general-purpose to dedicated AI inference. Training requires massive GPU computing power with distinct peaks and troughs, imposing extremely high requirements for GPU virtualization, memory pooling, and elastic scaling. Traditional general-purpose computing cannot meet these demands. The core competitiveness of cloud vendors has shifted from selling computing power to AI computing power scheduling and model operations. III. In the AI era, the strategy continuation and new iterations of the three major cloud vendors. None of the three vendors have abandoned their original advantages. Instead, they are making strategic upgrades by layering AI capabilities along their own DNA, with the route differences becoming increasingly clear. I. AWS: Sticking to hardware advantages, becoming the neutral computing power foundation in the AI era. Continuing the LEGO-style open philosophy, it does not self-develop proprietary large models but builds an AI computing power hub, integrating global mainstream models such as Claude, Llama, GPT, and OSS, allowing enterprises to choose and combine freely. It increases investment in AI chips and dedicated data centers, optimizing training and inference costs. Still emphasizing neutrality and openness, it does not compete with enterprises in the AI application market, allowing all model vendors and enterprise AI applications to rely on its underlying computing power for deployment. It defends market share through extreme scale. The core is to become the water, electricity, and coal of the AI era—no alignment, no lock-in. II. Azure: Leveraging enterprise ecosystem, deeply integrating AI with enterprise software, continuing the advantage of enterprise software integration. It fully connects OpenAI and Anthropic models with Office, GitHub, VS Code, and enterprise databases, embedding AI capabilities into the full-process tools enterprises already use. Enterprises do not need to change their existing IT architecture to directly invoke AI in office, development, and operations. At the same time, with its global cloud architecture and routing model, it enables on-demand model scheduling, drastically reducing costs (e.g., the $10 Copilot). The core is to lower the barrier for enterprise AI deployment, making AI a standard feature of enterprise software and firmly locking in existing enterprise customers. Growth relies on ecosystem stickiness. III. Google Cloud: Strengthening its service-oriented DNA, building a fully cloud-native AI architecture, continuing the route of service defining everything. Relying on Gemini and its self-developed architecture, it creates a full closed-loop of AI development and deployment on the cloud, promoting browser-based and cloud-based development, completely eliminating reliance on local tools. For AI unstructured data scenarios, it optimizes the synergy between big data and AI models, adapting to content understanding and recommendation scenarios for video, voice, etc. At the same time, it pushes forward with hybrid cloud and edge computing, trying to make up for its shortcomings in enterprise adaptability. Its route remains the most forward-looking. Aiming for future fully cloud-native AI scenarios, but its short-term deployment is still constrained by traditional enterprise architectures, and long-term value will take time to realize. IV. Core insights for cloud strategy in the AI era: I. There is no optimal strategy, only a strategy that fits one's DNA. None of the three vendors have overturned their original routes; instead, they amplify advantages to adapt to AI, proving that the core of cloud strategy is DNA continuation, not blind trend-following. II. Enterprise pain points are always the core. Azure's success fundamentally stems from catering to enterprises that do not want to rebuild IT but only seek smooth upgrades. AWS wins through neutrality, meeting diverse enterprise needs. Google's difficulty lies in being too advanced, disconnected from existing enterprise systems. III. AI is no longer an add-on feature of the cloud but the core engine. Cloud vendor competition has shifted from traditional computing power pricing and stability to AI computing power scheduling, model operations, and ecosystem integration. As the differences between models themselves diminish, engineering capabilities become the decisive factor. IV. Lessons from traditional software vendors. Oracle, due to its insistence on on-premises databases and resistance to cloud transformation, has fallen into trouble, proving that no matter how strong user stickiness is, it can be disrupted if one fails to adapt to technological trends. In the AI era, the core of enterprise services is adapting to new data forms and application logic.
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