Spiking Nets, Liquid AI Stay Niche

Hot take: these are worth learning if you want to work on edge AI, robotics, or brain-inspired research, but not because they are about to displace transformers. Their real value is in energy efficiency and temporal dynamics, not general-purpose SOTA.

• –SNNs have the clearest hardware story: event-driven sparsity can map well to neuromorphic chips and other low-power deployments.
• –The tooling and training ecosystem still lag standard deep learning, so prototyping is harder and benchmark wins are rarer.
• –Liquid neural networks are better viewed as continuous-time/state-space models for dynamic systems than as a new general-purpose paradigm.
• –For an undergrad project, a small event-camera, control, or robotics demo is a realistic way to learn the ideas without betting on mainstream industry adoption.
• –The thread’s early replies match the broader field: lots of conceptual appeal, but adoption stays niche unless hardware constraints make the trade-off worthwhile.