Look forward to the day when such flaws are largely eliminated!
In fact the current AI mainstream considers this as a research direction to avoid at all costs (they term it "the bitter lesson"). The strategy and rallying cry is roughly translated: you can achieve gee-wheeze results here and now by ignoring these deep problems that stymied generations of AI researchers.
Having been involved in both traditional machine learning and common sense AI in my grad school years, I've seen first-hand the limitations of a purely statistical approach. (Some of my past data augmentation work is being used to benchmark LLM reasoning.)
While most folks are too fixated by the 'quick wins' achieved by LLMs the trade-off is often a lack of non shallow reasoning. And I worry that many active researchers are glossing over these deeply rooted issues.