Symbolic failed because it was difficult for people to come up with the theory of mind first and lay down the formats and the functions and the rules to create the base knowledge and logic. And from what was created (which did have a lot of use, so I wouldn't say it amounted to nothing) they couldn't find a way to make it scale, and so it couldn't learn much or independently. On top of that, they were probably limited by hardware too. Researchers focus on ML because it's comparatively 'easy' and because it has produced results that so far can scale. What I suspect they'll try doing with LLMs is learning how they work and building structure into them after the fact, and finding that their performance has degraded or can't be improved significantly. In my opinion, neurosymbolic will be the ideal way forward to achieve AGI and ASI, especially for safety reasons, and will take the best of both symbolic and ML, and together helping with the drawbacks to both.