Modern AI models can generate complex software components, including backend, frontend, and infrastructure configurations, in a single evening. While this significantly accelerates the initial development phase, it does not eliminate the need for human developers.
The author tested AI-generated code by creating a booking interface. Although the code appeared functional and passed initial reviews, it contained common technical flaws such as race conditions in useEffect, improper error handling in asynchronous functions, and lack of edge-case management.
AI-generated code often lacks context regarding business logic and user experience trade-offs, such as determining appropriate debounce times or handling optimistic UI updates. These decisions require human judgment rather than just technical implementation.
The risk of errors increases significantly when scaling from a single frontend to a distributed system with multiple services and asynchronous communication. In such systems, individual components may appear correct, but systemic interactions can lead to complex, hard-to-detect failures.
The author emphasizes that the primary skill for developers working with AI is the ability to critically evaluate and test generated code. Instead of trusting a working demo, developers must actively attempt to break the system to identify hidden architectural and logic issues.
