AI Coding Tools Feel Different Once Repos Grow Larger

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

But after working on several AI-assisted projects, I noticed something more important:

The biggest bottleneck is no longer coding itself.

It’s context management.

Once repositories grow larger, AI tools start behaving very differently.

The real differences become:

• repo understanding
• multi-file coordination
• remembering dependencies
• architecture consistency
• workflow continuity

Some tools feel amazing for:

• rapid prototyping
• brainstorming
• UI iteration

Others become much stronger for:

• large refactors
• repo-wide cleanup
• dependency-aware editing

After around 40+ files, the gap becomes very noticeable.

Curious whether other developers experienced the same thing while scaling AI-assisted projects.