[D] Why are serious alternatives to gradient descent not being explored more?

A viral discussion on Reddit's r/MachineLearning highlights a growing researcher consensus: gradient descent and backpropagation may be fundamental bottlenecks. Many argue these methods cannot properly solve continual learning (learning new tasks without forgetting) or causal reasoning. Despite this, most published work focuses on gaming benchmarks with more data rather than exploring radically different learning architectures from the ground up, creating a perceived innovation gap in the field.