AI 越用越亏本，企业哪里做错了？

Core Insights - The article discusses the dual emergence of "AI bubble theory" and "AI utility theory" in 2025, highlighting that the expansion of the AI industry has not fully translated into practical value or efficiency, both in consumer applications and enterprise returns [2] - The current bottleneck in AI applications is not the "intelligent capability" but rather the "engineering capability" required for deployment in production environments [2][3] Group 1: AI Application Paradigms - The need to rethink AI application paradigms to enhance core efficiency has become a focal point of discussion, with Amazon Web Services (AWS) aiming to build a customizable AI framework for enterprises [3][4] - The introduction of Agentic AI technology aims to automate the deployment of agents, addressing the inefficiencies enterprises face in utilizing AI tools [5][10] Group 2: Agentic AI Features - Agents, built on large models, can perform complex tasks through a complete cycle of perception, thinking, decision-making, execution, and feedback, thus simplifying and automating many tedious processes [5][10] - An example provided by AWS CEO Matt Garman compares AI agents to children that need to be nurtured and trained, emphasizing the balance between oversight and autonomy [6] Group 3: Specific Agent Applications - AWS introduced three advanced agents focused on efficiency optimization, allowing users to set broad goals while the agents autonomously seek to achieve them [7] - The Kiro autonomous agent is designed for software development, addressing issues like context switching and manual coordination of code changes [9] - Amazon Security Agent and Amazon DevOps Agent enhance security and operational efficiency throughout the development lifecycle, transforming reactive maintenance into proactive optimization [9] Group 4: Future of AI Operations - The future of AI applications lies in creating a true "AI operating system" that integrates seamlessly with enterprise processes, enhancing automation while ensuring flexibility and security [11][12] - Amazon Bedrock serves as a foundational platform that supports the development and management of agents, allowing for the integration of enterprise workflows and compliance strategies [12][15] - The efficiency of agents stems from their ability to execute actions, but this also introduces risks that necessitate robust security and evaluation systems [13][15] Group 5: Conclusion - The article concludes that for AI to transition from a tool to an integral part of organizational capabilities, all components—computing power, models, and frameworks—must work in harmony [15] - AWS is focused on addressing every pain point and optimizing core metrics to provide a solid foundation for enterprises to embrace AI, moving towards a collaborative role for AI within organizations [15]