llm/122b8d72-a8a3-4fcf-8eca-6a52786d1a8b/topic-3-82eba11f-2472-487b-88d1-d4d726492384-output.json
While AI tools are hailed as a "10x" productivity multiplier for greenfield development, allowing developers to rapidly execute once-unjustifiable small projects, many users report hitting a "wall" around the 10,000-line mark where models begin to introduce regressions and convoluted logic. Some argue that this threshold is not absolute, suggesting that modular architecture and constrained scoping can allow AI to remain effective even in massive codebases exceeding 100,000 lines. However, skeptics maintain that "vibe coding" from scratch often leads to a messy "babysitting" phase where the AI fails to grasp intertwined dependencies or the nuanced requirements of legacy hardware. Ultimately, the consensus suggests that while AI excels at documentation and initial scaffolding, it still requires significant human oversight to maintain the structural integrity and long-term stability demanded by enterprise-level software.