The point isn't that you can trick an LLM, but that their capabilities are more strongly tied to training data than context. That's to say, when context and training disagree, training "wins". ("wins" isn't the correct wording, but hopefully you understand the point)
This poses a problem for new frameworks/languages/whatever that do things in a wholly different way since we'll be forced to rely on context that will contradict the training data that's available.
What is an example of a framework that does things in a wholly different way? Everything I'm familiar with is a variation on well explored ideas from the 60s-70s.
If you had someone familiar with every computer science concept, every textbook, every paper, etc. up to say 2010 (or even 2000 or earlier), along with deep experience using dozens of programming languages, and you sat them down to look at a codebase, what could you put in front of them that they couldn't describe to you with words they already know?
Even the differences between React and Svelte are big enough for this to be noticeable. And Svelte is actually present in the training data. Given the large amount of react training data, svelte performs significantly worse (yes, even when given the full official svelte llms.txt in the context)
But it doesn't pose a problem. You are extrapolating things that are not even correlated.
You started with 'they can't understand anything new' and then followed it up with 'because I can trick it with logic problems' which doesn't prove that.
Have you even tried doing what you say won't work?
This poses a problem for new frameworks/languages/whatever that do things in a wholly different way since we'll be forced to rely on context that will contradict the training data that's available.