AI TOP 100 Competition Retrospective: Building a Full-Stack System in 2 Weeks with AI-Native Development

In the retrospective on system development for the AI TOP 100 competition, the AI-native development process shared practical experiences that surpassed the limitations of traditional development methodologies. The core shift was changing the question to 'How far can we go with AI?', communicating via AI-generated prototypes (PoCs) instead of documents or meetings, and drastically shortening the feedback cycle.

Development Efficiency and Infrastructure Utilization

System development, which typically takes several months, was completed in just 2 weeks for both the preliminary and final rounds. Some developers consumed 200 million tokens per day, achieving overwhelming output volumes. By applying hexagonal architecture, AI automatically generated boilerplate code, allowing the team to focus on discussions about business value. Internal guide documents were also fed to the AI to automatically generate code for authentication and storage integration.

Dissolution of Role Boundaries and Collaboration

Technical barriers between roles were lowered, with backend developers and data engineers using AI to implement frontend UIs and service platforms. Team members recognized AI as a 'repetitive task handler' or a 'trustworthy colleague,' while establishing a division of labor where humans made final decisions based on security vulnerability assessments and domain knowledge.

Limitations and Implications

While AI excels at prototype generation and productivity enhancement, ensuring the maintainability and security of the final system remains a human responsibility. Recognizing the risk of technical debt in AI-generated code and managing quality through rigorous verification and documentation were cited as key factors for project success.