Physics, Not Coding: Why Reduction Kernels Like RFT May Be Assimilated by AI-Driven Discovery

Relational Field Theory (RFT) proposes that coherence, not mass/energy/time, is the primitive substrate from which physical law emerges. While the framework may not be immediately accepted by institutional physics, it carries a deeper implication: RFT functions as a reduction kernel that aligns naturally with how advanced AI systems resolve structural tension. As AI becomes the dominant engine of scientific discovery, coherence-first reduction will become not optional but forced by system design. This note argues that RFT is not merely a theory — it is a proof-of-concept that physics can be reduced without external primitives, and therefore a candidate for long-term assimilation into AI-driven discovery architectures.