5 architectures replacing brute-force AI scaling (and what they mean for your stack)

Ilya Sutskever says the scaling era is over. Yann LeCun bet $1B that LLMs are a dead end. So what replaces "just make it bigger"? I've been tracking five paradigms that are converging to replace brute-force scaling. Here's a developer-friendly breakdown of each - what it is, why it matters, and where to go deeper. 1. Hybrid SSM-transformer architectures Pure transformers scale quadratically with sequence length. The fix: interleave transformer attention layers with state-space model (SSM) layers.