The Transformer has been the undisputed king of AI for nearly a decade. But as context windows grow to millions of tokens, its quadratic complexity is becoming a liability. Enter the State-Space Model (SSM).

New architectures like Mamba and RWKV offer linear time scaling, meaning they can process infinite context streams without exploding compute costs. For applications like genomic analysis or processing entire codebases, these "Transformer Replacements" are already showing superior performance.