AI 越用越亏本，企业哪里做错了？

Core Insights - The year 2025 marks the rise of both "AI bubble theory" and "AI utility theory," which, despite appearing contradictory, share a common core [2] - The expansion of the AI industry has not fully translated into utility and value, with both consumer applications and enterprise efficiency lagging behind market expectations [2] - The current bottleneck in AI applications is not the "intelligent capability" but rather the "engineering capability" needed for low-cost, scalable deployment in production environments [2] Group 1: AI Application Paradigm - The focus has shifted to rethinking AI application paradigms to enhance core efficiency, with Amazon Web Services (AWS) aiming to build a customizable AI framework for enterprises [4] - The introduction of Agentic AI technology aims to automate the deployment of agents, addressing the inefficiencies faced by enterprises in utilizing AI [4] - 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 workflows [4][5] Group 2: Agent Functionality and Examples - For e-commerce, training an agent to create an automated customer service system can be achieved by providing existing product databases and customer records, allowing the agent to learn from this data [5] - AWS's three advanced agents focus on efficiency optimization, enabling users to set broad goals while the agents autonomously seek to achieve them [5] - The Kiro autonomous agent addresses issues like context switching and manual coordination in software development, maintaining context across multiple interactions [6] Group 3: Security and Compliance in AI - Amazon Security Agent and Amazon DevOps Agent enhance security throughout the development lifecycle and automate operations, transforming reactive maintenance into proactive optimization [8] - These agents signify a trend towards integrating enterprise processes and experiences into AI knowledge, which can be automatically applied to workflows, improving efficiency [8] Group 4: Future of AI Operations - The future of AI applications involves creating a true "AI operating system," with agents being a crucial paradigm that raises questions about flexibility, security, and efficiency evaluation [9] - Amazon Bedrock serves as a foundational platform for building agents, allowing for the integration of various models and ensuring compliance and security [9][10] - The efficiency of agents stems from their ability to execute actions, but this also introduces risks that necessitate robust security and evaluation systems [10] Group 5: Infrastructure and Support for AI - AWS provides comprehensive support for AI agents across infrastructure, models, data, and tools, ensuring that AI is scalable, understandable, and trustworthy [12] - The analogy of AI utilization as a car illustrates that computational power is the fuel, models are the engine, and Amazon Bedrock is the overall powertrain, with agents acting as control systems [12] - The goal is to transform AI from a tool into an integral part of organizational capability, helping enterprises unlock value [12]