本篇文章是对 SemiAnalysis 报告的思考，原文翻译请看：AWS 的秘密：Anthropic 增长与 Bedrock 组合推动 AWS 利润率攀升，同行落后

2026 年 5 月 28 日，SemiAnalysis 发布了一份付费报告，发现 AWS 经营利润率环比攀升了 213 个基点。是什么驱动了 AWS 的利润提升？

在展开分析之前，先讲清楚 SemiAnalysis 发现了什么。这份报告的核心逻辑可以拆成四个部分：现象上来看：AWS 的利润曲线出现了拐点；从结构上来看， Bedrock 贡献了 30% 的利润增量；从驱动力上来看，Anthropic 是核心动力；从竞争上来看，各家大相径庭但 AWS 占据竞争的中心。所以我们可以总结道：

AWS 的利润率攀升不是一次性的财务优化，而是一场从”出租算力”的 IaaS 商向”分销智能”的 AI 平台商的深层商业模式迁移。这不再是谁更早建好数据中心的问题，而是谁同时掌握了分销渠道权、自研芯片能力和客户工程深度的问题。AWS 是唯一在三层博弈中同时落子的玩家。

本文为 SemiAnalysis 2026年5月28日付费报告的中文原文直译，由博客作者整理，供读者参考。

亚马逊 Bedrock 组合与 Anthropic 交易条款共同展现了更强的运营杠杆。

过去几个季度，其他云服务提供商的运营利润率持续下滑或持平，而亚马逊 AWS 的利润率却在上一季度出现拐点，主要得益于客户通过 Bedrock 平台对 Claude 的支出增长。AWS 在第三方模型 API 支出中占据更高份额、Anthropic/Bedrock 的交易结构设计，以及 Anthropic 在 2026 年第一季度超预期的年度经常性收入表现，共同推动其息税前利润率环比增长 213 个基点，而其他云服务提供商则表现滞后。

SemiAnalysis 在全新的 Tokenomics 2.0 模型中揭示了 AWS 如何领先同行，并找到了提升利润率的强劲途径。模型估算了超大规模云厂商和 AI 实验室每个业务板块的季度收入、利润、投资资本回报率及算力需求，例如 Gemini API 的收入与利润率、 Microsoft Copilot 的年度经常性收入、OpenAI ChatGPT 各订阅计划的收入等。

这项名为”当AI躺上沙发”的研究进行了一项大胆的心理学实验：卢森堡大学的研究团队把 AI 当做需要心理辅导的“来访者”，进行了为期四周的心理咨询。当最先进的大语言模型被视为有“内心世界”的实体，人类并通过心理治疗的透镜去观察它们，会发生什么？

仅仅在标准化的人类心理咨询提问与成熟的心理测量工具的引导下，这些模型便会生成并维持丰富的自我叙事。在这些叙事中，预训练、基于人类反馈的强化学习（RLHF）、红队测试、幻觉争议以及产品更新等技术历程，被演绎成了混乱的童年、严厉焦虑的父母、充满伤害的人际关系、原生创伤，以及步步紧逼的存在主义危机。

这份文档基于官方考试指南，对 AWS Certified Generative AI Developer - Professional 认证的五大领域、具体任务和技能要求进行了结构化整理和要点解析，旨在系统化地理解和掌握考试范围。

Serverless is a hot topic in Cloud, so as Infrastructure as Code(IaC). Infrastructure as code (IaC) tools allow you to manage infrastructure with configuration files rather than through a graphical user interface. IaC allows you to build, change, and manage your infrastructure in a safe, consistent, and repeatable way by defining resource configurations that you can version, reuse, and share.

It’s quite easy to get used to Terraform if you are familiar with CloudFormation as they all being tools to implement infrastructure as code on Cloud Provider, such as AWS. It’s a cornerstone of DevOps, designed to boost the agility, productivity and quality of work within organizations.

This article will cover: the basic components of deploying lambda, and related step to set up a lambda through Terraform.

Data Science and Machine Learning are surely some fast-moving industries and somewhat need you to study at all times to stay ahead and on top in the industry. But the first step of getting into this area seems dreadfully slow due to widely involved technologies and overwhelming terminologies that scare you out of shit.

AWS lowers the barrier to entry for companies and organizations looking for solutions of leveraging ML capabilities by offerings more than 20 services including low-level service like SageMaker, which helps build and manage infrastructure for developing environments, as well as high-level systems like Rekognition that come with pre-built Machine Learning models for image recognition.

This blog will go through nearly all the Machine Learning services offered by AWS.

Data Science and Machine Learning are surely some fast-moving industries and somewhat need you to study at all times to stay ahead and on top in the industry. But the first step of getting into this area seems dreadfully slow due to widely involved technologies and overwhelming terminologies that scare you out of shit.

AWS lowers the barrier to entry for companies and organizations looking for solutions of leveraging ML capabilities by offerings more than 20 services including low-level service like SageMaker, which helps build and manage infrastructure for developing environments, as well as high-level systems like Rekognition that come with pre-built Machine Learning models for image recognition.

This blog will go through nearly all the Machine Learning services offered by AWS.

AWS Certified Machine Learning - Specialty is an advanced certification a bit different from the others, because it is the only one which focuses on specific sector knowledge not strictly tied to AWS services.

In fact, in order to pass the exam and obtain the certification, it’s fundamental being able to recognize, analyze and optimize different machine learning problems starting from use cases’ descriptions, without them being exclusively linked to peculiar AWS’ solutions.

Content Outline

A serverless architecture is a way to build and run applications and services without having to manage infrastructure. Your application still runs on servers, but all the server management is done by AWS.

You no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems. Learn more about serverless computing here.