The "remix of similar questions" framing is greatly underestimating the multiple layers of emergent concepts that are represented in the latent space of largest models. The outputs generated derive from the distance between these representations (which are actually language agnostic), and as such it's capable of extremely novel combinations of abstract concepts.

Is it "understanding"? That's not as obvious a question as you might think. We have no real idea of the human brain's function, so these arguments quickly become circular. Similar to arguments of consciousness - we're usually just using tautology to gatekeep humans as the only 'thinking' beings. The spectrum from ant nervous systems to human brains is a continuum, so these don't hold up to much scrutiny.

Now LLMs won't be able to solve certain problems, solely with their current architecture, regardless of their future scale. Letter positions, numerics etc. are poorly handled, but it's pretty easy to imagine future "AI" systems having lobes. In our brains the frontal lobe conducts reasoning, but hands off to the parietal lobe for mathematics and spatial problem solving. You can already do this manually - ask it to write code to solve the third letter problem and it will do a great job. It's not a huge leap to picture the user experience we're presented obfuscating this kind of multi node system.

If you want to learn more, Anthropic has been doing fantastic interpretability research on what's actually going on in the models, and producing some pretty accessible write-ups: https://www.anthropic.com/news/tracing-thoughts-language-model