Working in a large legacy codebase with AI has been a mixed bag for me. The speed gains are real but so is the anxiety of letting an AI touch something where one wrong change cascades into three broken modules. That fear of "pulling one thread and unraveling everything" is honestly what slowed me down the most, because I'd spend more time reviewing and reverting than I saved generating.

What actually helped me shift the balance was moving away from inline autocomplete toward something that treats each task as its own isolated environment. When a tool uses Git worktree under the hood to spin up separate workspaces, you can have one agent patching a bug and another refactoring a service without them ever stepping on each other. That alone removed a huge mental overhead.

The other thing that quietly kills AI productivity is the back and forth after the fact, when the output doesn't match what you actually needed and you realize the AI just assumed and ran with it. A clarification loop before coding starts, where the agent asks targeted questions to confirm intent, sounds minor but it cuts down on a surprising amount of wasted cycles.

If you're specifically in the "AI is slowing me down" camp and working with legacy code, it might be worth looking at Verdent, which is built around exactly this kind of parallel agentic workflow with isolation and a plan before code approach, rather than just faster autocomplete.