The Architecture Decision Inside Every LLM

The transformer architecture didn't win because it modeled language better than every alternative in some abstract sense — recurrent networks had a defensible theoretical case too. It won because attention layers can be computed in parallel across an entire sequence at once, while recurrent architectures process one token at a time. On modern hardware, that single property turned into an enormous compute-efficiency advantage.

This is a pattern worth internalizing beyond AI: the architecture that wins a platform shift is rarely the theoretically purest one. It's the one whose bottleneck lines up with the resource that's about to get cheap. Transformers arrived just as GPU parallelism was becoming abundant, and the fit between the two is most of the story.

The practical lesson for anyone building on top of this layer is to stop asking "which model is smartest this month" and start asking which architectural choices are aligned with the next resource curve — because that alignment, not raw cleverness, is what compounds.