Most AI systems process tokens. Very few process meaning. That distinction may define the next era of intelligence. I’ve been working on a framework called "Mathematics of Meaning" — an attempt to model meaning not as static symbols, but as measurable structure: • coherence • semantic geometry • contextual drift • conceptual interference • topological relationships between ideas Today’s AI architectures are extraordinarily powerful statistically, yet they remain fragile semantically. They predict well. But prediction is not understanding. ❓ What if meaning itself has mathematical behavior? What if concepts occupy structured spaces rather than isolated symbolic states? What if ambiguity, contradiction, and drift can be modeled geometrically? This opens a very different direction for AI: coherence-based reasoning semantic stability analysis admissible execution systems context-governed intelligence topology-aware cognition The long-term implication is larger than language models.…