当MiniMax（稀宇科技）等中国通用人工智能企业加速冲刺上市、国内互联网大厂在AI Agent（人工智 能智能体）领域密集落子，全球AI产业正从"模型竞赛"迈入"落地深水区"。 "AI Agent落地已进入产业拐点，编码开发提效与生产力升级成为两大核心场景。"亚马逊云科技大中华 区解决方案架构总经理代闻近日在接受《每日经济新闻》记者采访时表示，当前企业对AI的需求已 从"用不用"转向"怎么用"，组织流程重构与工具赋能协同成为落地关键。 "当前Agent落地已形成两大高共识场景——编码开发提效与生产力升级。"代闻表示，这一判断背后， 是明确的市场需求。 亚马逊云科技正试图用内部超4万个Agent应用的实践验证这一逻辑。但面对中国市场的独特生态， 其"模型出海+本地方案"的双线策略，能否与国内玩家共同推动产业进入规模化落地新阶段，仍需时间 检验。 编码开发提效与生产力升级，Agent双线落地 三年前，行业热议AGI（通用人工智能）何时到来；如今，企业已清晰认识到模型的局限性与差异化价 值。 AI Agent的产业价值已得到市场阶段性验证。Lang Chain发布的《AI Agent工程状态报告》显示，57.3% ...

Core Insights - Amazon Web Services (AWS) is focusing on making computing power truly usable and enabling Agents to operate effectively, rather than chasing short-term profits from selling computing power [2][38] - AWS maintains a leading position in the global cloud market with a market share of 37.5%, significantly ahead of its closest competitor [2][39] - The annual recurring revenue (ARR) for AWS is projected to reach $132 billion by December 2025, reflecting a 20% year-over-year growth [2][39] Competitive Landscape - AWS faces intense competition from Microsoft Azure, Google Cloud GCP, Oracle OCI, and CoreWeave, which are securing long-term contracts with major clients through investments and computing power collaborations [3][39] - The concept of "computing power financialization" is creating short-term pressure on AWS's stock and public perception [3][39] Technological Trends - The integration of full-stack AI, including chips and models, is becoming increasingly important for attracting enterprise clients [3][40] - The rise of Agentic AI is identified as a new battleground, with billions of Agents expected to emerge in the future [3][40] AWS's Strategic Response - At the re:Invent 2025 conference, AWS announced new products aimed at helping enterprise clients quickly implement Agents [4][40] - CEO Matt Garman emphasized that valuable Agents require four core components: AI infrastructure, AI inference platforms, data, and Agent development tools [4][40] Cost Efficiency Initiatives - AWS is developing its own AI chips to reduce the total cost of ownership (TCO) for computing infrastructure [8][44] - The newly launched Trainium 3 chip, built on a 3nm process, can produce five times more Tokens per megawatt compared to its predecessor and reduce training costs by up to 50% [9][45] Product Development - AWS has deployed over 1 million Trainium chips, which are expected to generate billions in revenue annually [11][47] - The Amazon Nova 2 series of self-developed models aims to provide cost-effective solutions for enterprises, with a focus on low-cost processing of simpler tasks [12][51] Market Positioning - Amazon Bedrock, AWS's model platform, integrates models from various vendors, allowing enterprises to utilize multiple models efficiently [16][52] - The company is positioning Amazon Bedrock as a significant growth driver, with expectations of it matching the revenue contribution of EC2 in the long term [19][55] Agent Development Tools - AWS launched Amazon Bedrock AgentCore, a standardized toolset for developing and deploying Agents, which has seen over 200,000 SDK downloads shortly after its release [20][56] - The company is also introducing official Agent tools, such as Security Agent and DevOps Agent, to enhance internal operations and customer offerings [23][59] Long-term Vision - AWS is focused on solving current customer pain points rather than pursuing speculative short-term gains, reflecting a pragmatic approach to technology development [32][34] - The company aims to build a comprehensive Agent infrastructure that can drive exponential growth in computing power consumption through user interactions with Agents [26][29]