At first, I thought AI coding tools were mainly competing on code generation quality. But after building several AI-assisted projects, I noticed something more important: The biggest bottleneck is no longer coding itself. It’s context management. Once projects grow larger, AI tools start behaving very differently. What matters now is: repo understanding multi-file coordination remembering dependencies preserving architecture consistency rollback safety workflow orchestration In small demos, most tools feel impressive. But once my projects passed around 40+ files, the differences became much clearer. For example: Windsurf helped me move faster during: rough prototyping brainstorming UI iteration quick experimentation But Codex became much stronger for: repo-wide cleanup multi-file refactoring dependency-aware edits context-heavy modifications One thing I learned: The future AI coding battle probably won’t be: “Which model writes code better?” It will be: “Which system manages large-scale context better?” That changes how I evaluate AI tools now. I no longer judge them only by: raw code output benchmark scores first impressions I pay much more attention to: long-session stability repo awareness workflow continuity architecture preservation AI coding is slowly becoming less about autocomplete… …and more about operating systems for development workflows. Curious how other developers are experiencing this.