I (like vannevar apparently) didn't feel Cyc was going anywhere useful, there were ideas there, but not coherent enough to form a credible basis for even a hypothesis of how a system could be constructed that would embody them.
I was pretty impressed by McCarthy's blocks world demo, later he and a student formalized some of the rules for creating 'context'[1] for AI to operate within, I continue to think that will be crucial to solving some of the mess that LLMs create.
For example, the early failures of LLMs suggesting that you could make salad crunchy by adding rocks was a classic context failure, data from the context of 'humor' and data from the context of 'recipes' intertwined. Because existing models have no context during training, there is nothing in the model that 'tunes' the output based on context. And you get rocks in your salad.
[1] https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&d...
This seems like a high bar to reach.
We all know that symbolic AI didn't scale as well as LLMs trained on huge amounts of data. However, as you note, it also tried to address many things that LLMs still don't do well.