AI-generated apps currently fall short of building sustainable businesses. While tools like Replit and Lovable excel at creating quick prototypes, they compromise on code quality and robust architecture. The author emphasizes the need for a shift towards prioritizing clean, maintainable code over flashy features. The differences between standard AI-generated code and production-grade code highlight the importance of structure and best practices. For instance, poor code often features hardcoded values and mixed responsibilities, making maintenance difficult, while high-quality code separates concerns, employs type safety, and uses reusable components. Building an app is only a small part of the work; the majority involves debugging, scaling, and ensuring reliability. The risks become apparent when businesses rely on AI-generated code without understanding it. A hidden bug could lead to significant customer trust issues if no one on the team can troubleshoot effectively. The author argues that good code should be straightforward, reusable, and easy to understand months later. Unfortunately, AI tools today often produce code that lacks these qualities, focusing instead on immediate correctness without consideration for long-term maintainability. The author critiques the current incentive structure for AI models, which rewards short-term solutions rather than code quality. As a result, AI tends to generate disorganized code, akin to "spaghetti," that is hard to work with. Moving forward, the focus must shift to enabling AI to create high-quality, maintainable software. This means treating AI like a junior developer that requires guidance and oversight. To achieve this, architectural direction, thorough reviews, and extensive testing are necessary. Woz aims to produce software that resembles the output of a senior development team, emphasizing the importance of code quality as a foundation for successful AI-augmented software development.