Productivity. You generate the skeleton of the code with Codex/Claude Code/et. al. and refactor it manually. It's kind of unlikely that an AI agent will be able to one-shot every bit of code in the exact way you want, even with a fat AGENTS.md file. With a smart AI-native IDE like Zed, it will quickly be able to pick up what manual change you intent to do without you fully typing out anything, especially if they're repetitive. This helps enormously when you're debugging or profiling your code.

the obvious answer is because it's easier , faster, and more efficient to flip a true to false right in front of you than it is to prompt an llm.

if your response is "my prompts don't produce code that needs values flipped, ever." then I would wager you're only touching very simple things with an LLM.

for me I don't care about the token cost and prompt writing so much as the fact that it's just faster to change 0 to 1 and leaves me twiddling my thumbs for an llm output less.

That’s like asking why anyone would use IDE autoformatting, linting, or build tools rather than constantly swapping to a terminal to run their command line versions. As in, why use tool integration in an integrated development environment? Because that’s the entire point. Classic IDE refactoring and code generation tools are limited to explicitly programmed operations, but a well-integrated LLM can do much more and smarter manipulations without you having to context switch and explain the context of what you want done.

For Windsurf at least, it makes it easier to control context. I can simply drag and drop a file from the IDE into the chat.

I can also click on a file referenced by the AI and have it open immediately in the IDE so that I can inspect it.

Finally, it is a pain to write long, multi-line prompts in a CLI where you can't easily click around to edit different parts.

The primary weakness I've found in IDE based UI is that it struggles to get through the corporate security in order to run commands.

> what's the point

Tab completion.

Smart model can cut down time to write complex firewall yaml dramatically, relying both on the existing file and the ugly draft (eg comma delimited details of the rules I need) I put out. It makes it 5 minutes lead time and 20 presses of tab instead of writing a shell/python full of edge cases or just copying existing rules as a template and laborously editing them -- smart model knows what the specific firewall needs.

But I'm not a developer, so I use both - haiku via github for tab completion and CC for cli.

For me I need to compare the code generated before committing. Also I need to read markdown plans generated for review before commit to execution. VSCode CC extension also generate clickable links to the file directly if the query has something to do with it.

All of them are valid usecase of VSCode CC extension for me.