Over the past four years, the most significant breakthroughs in large language model (LLM) products have been driven not by increasing model parameters, but by rethinking how tokens flow through the system. This insight, drawn from a recent analysis, identifies key patterns: CoT/PAL determines where uncertainty is placed, ReAct/CodeAct controls how much is written per forward pass, and Voyager/Skills manages what persists across runs. These architectural choices have reshaped user experiences and product capabilities. For developers and product leaders, understanding this token IO architecture is now more critical than chasing larger models. The shift from scaling laws to flow design represents a fundamental change in how we build and optimize AI applications.
An analysis arguing that token flow design, not model size, has driven LLM product breakthroughs over four years.