> They can help with those tasks because there are decades of published research on them.
No.
I suspect the underlying meme behind this is "LLMs can't think, they are just stochastic parrots". If you define an LLM to be the thing the model builders train by ingesting and compressing information on the internet, in a way that yields a very convenient if somewhat unreliable search engine, then that's true. But if you define them that way, the modern models like Opus, Gemini, GLM and so on aren't pure LLMs, and haven't been for a while. They still have an LLM inside them, but have other things bolted on like attention heads, context windows, and Chain of Thought. These things outperform humans at some tasks that require "thinking".
Glasswing is an example. Before Glasswing human "researchers" could find a solution to a particular problem, the "lets find an exploit in this software" problem, as a particular rate. Now with Glasswing, it's orders of magnitude faster than that rate. The same thing has happened in other areas, like protein folding and weather prediction. These models aren't reproducing something they've seen before. They are producing things we consider new - like creating an exploit for a vulnerability they discovered.
With all the evidence around if you are still hanging onto the idea these models can't help with many tasks by doing what we call "thinking", you are in denial.