AI-Native 的 Infra 演化路线：L0 到 L5

Core Viewpoint - The ultimate goal of AI is not just to assist in coding but to gain control over the entire software lifecycle, from conception to deployment and ongoing maintenance [6][54]. Group 1: AI's Impact on Coding - The critical point where AI will replace human coding is expected to arrive within the next 1-2 years [7]. - AI's capabilities should extend beyond coding to encompass the entire software lifecycle, including building, deploying, and maintaining systems [7][10]. - Current backend systems are designed with the assumption of human programmer involvement, making them unsuitable for AI use [7][12]. Group 2: Evolution of AI-Native Infrastructure - An evolutionary model (L0-L5) is proposed to describe the progression of AI infrastructure [7][14]. - The future software paradigm will trend towards "Result-as-a-Service," where human roles shift from engineers to quality assurance, while AI handles generation and maintenance [7][54]. - AI is transitioning from being a tool user to becoming a system leader, indicating a significant shift in its role within software development [18][54]. Group 3: Challenges in Current Systems - Existing backend tools are fundamentally designed for human interaction, which limits AI's operational efficiency [12][13]. - Current systems often present ambiguous error messages that are not machine-readable, creating barriers for AI [12][13]. - The lack of standardized error codes and automated recovery mechanisms in traditional systems hinders AI's ability to function autonomously [12][13]. Group 4: Stages of AI Capability Development - The L0 stage represents AI being constrained by traditional infrastructure, functioning like an intern mimicking human actions [18][20]. - The L1 stage allows AI to perform actions through standardized interfaces but lacks a comprehensive understanding of system architecture [21][22]. - The L2 stage enables AI to assemble systems by understanding module relationships, marking a shift from task execution to system assembly [27][30]. Group 5: Future Infrastructure Requirements - To achieve true AI-Native infrastructure, systems must be designed to eliminate human-centric assumptions and allow AI to operate independently [14][57]. - The infrastructure must provide a complete system view, enabling AI to query and manage all components effectively [31][45]. - AI must have the autonomy to design and manage the entire infrastructure, transitioning from a service manager to a system architect [39][45].