[plastic-enjoyer]

> There is huge pressure to prove and scale radical alternative paradigms like memory-centric compute such as memristors, or SNNs, etc. That's why I am surprised we don't hear a lot about very large speculative investments in these directions to dramatically multiply AI compute efficiency.

Because the alternatives lack the breakthroughs that give them an edge against current-state AI and don't generate the hype like transformers or diffusion models. You have stuff like neuromorphic hardware that is hardly accessible and in its infancy, e.g. SpiNNaker. You have disciplines like Computational Neuroscience that try to model the brain and come up with novel models and algorithms for learning, which, however, are computational expensive or just perform worse than conventional deep learning models and may benefit from neuromorphic hardware. But again, access is difficult to such hardware.