> I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.

LLMs can do neither reliably because to do that you need understanding which LLMs don't have. You need to learn from the codebase and the project, which LLMs can't do.

On top of that, to have the big picture LLMs have to be inside your mind. To know and correlate the various Google Docs and Figma files, the Slack discussions, the various notes scattered on your system etc.

They can't do that either because, well, they don't understand or learn (and no, clawdbot will not help you with that).

> The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.

These are not limitations of tooling, and no, LLM developers are not even close to overcoming, especially not "constantly". The only "overcoming" has been the gimmicky "1 million token context" which doesn't really work.