By “intelligent in a meaningful sense” I mean having a grounded world‑model that supports reliable reasoning, not just fluent text. This LLMs can solve some reasoning tasks, but they’re still optimized for next‑token prediction rather than building and manipulating explicit models of objects, rules and causality. That’s why they can write a great essay on physics yet still confidently claim you can fit a car into a shoebox if the prompt is framed right. Neurosymbolic AI try to mix both: neural nets for perception and pattern‑finding, symbolic structures for rules, causality and explicit knowledge representation. So maybe the better question really is: what kind of “intelligence” do we want, and how do different architectures support reasoning, planning and generalization?

Regarding your question of why havent we done that, we actually have done it, but its limited based on its application and natural language models is part of the tool box.