Why AI Coding Tools Feel Different Once Projects Become Larger

At first, I thought most AI coding tools were mainly competing on code generation quality.

But after working on several AI-assisted projects, I noticed the real challenge changes once repositories become larger.

The bottleneck becomes:

• context handling
• multi-file coordination
• architecture consistency
• debugging across large codebases

Small demos rarely expose these weaknesses.

But larger repositories make the differences between tools much more visible.

I recently shared more detailed thoughts here:
https://aitoolsreviewhub.hashnode.dev/ai-coding-tools-feel-very-different-once-repositories-grow-larger