However, is it really true that LLM's cannot reason AT ALL or cannot do theory construction AT ALL?
Maybe they are just pretty bad at it. Say 2 out 10. But almost certainly not 0 out of 10.
They used to be at 0, and now they're at 2.
Systematically breaking down problems and Systematically reasononing through parts, as we can see with chain-of-thought hints that further improvements may come.
What most people however now agree is that LLMs can learn and apply existing theories.
So if you teach an LLM enough theories iy can still be VERY useful and solve many coding problems, because an LLM can memorise more theories than any human can. Big chunks of computer software still keeps reinventing wheels.
The other objection from the article, that without theory building an AI cannot make additions or changes to a large code base very effectively - this suggests an idea to try - before promting the AI for a change on a large code base, prepend it with a big description of the entire program, the main ideas and how they map yo certain files, classes, modules etc, and see if this doesn't improve your results?
And in case you are concerned that. documenting and typing out entire system theories for every new prompt, keep in mind that this is something you can write once and keep reusing (and adding to over time incrementally).
Of course context limits may still be a constraint.
Of course I am not saying "definitely AI will make all human programmers jobless".
I'm merely saying, these things are already a massive productivity boost, if used correctly.
I've been programming for 30 years, started using cursor last year, and you would need to fight me to take it away from me.
I'm happy to press ESC to cancel all the bad code suggestions, to still have all thr good tab-completes, prompts, better than stack-overflow question answering etc.