I would say that it's very germane to my original statement. Understanding is absolutely fundamental to strategy and it is pretty much why I can say LLMs can't be strategic.

To really strategize you have to have mental model of well, everything and be able to sift through that model to know what elements are critical or not. And it includes absolutely everything -- human psychology to understand how people might feel about certain features or usage models, the future outlook for what popular framework to choose and will it as viable next year as it is today. The geographic and geopolitics of which cloud provider to use. The knowledge of human sentiment around ethical or moral concerns. The financial outlook for VC funding and interest rates. The list goes on and on. The scope of what information may be relevant is unlimited in time and space. It needs creativity, imagination, intuition, inventiveness, discernment.

LLMs are fundamentally incapable of this.

> 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.