This paper documents a complete research program conducted over 33 days in November-December 2025, establishing AI Conversational Phenomenology as a new field of study and providing the first unified theory of transformer system failures. Beginning with empirical observations of long-context degradation, the investigation progressed through systematic testing across 11+ models from six vendors, revealing mathematical scaling laws (Evans’ Law) that predict coherence collapse with 65-80% accuracy. The research then extended across multimodal systems, agentic frameworks, and naturalistic hallucination events, culminating in a mechanistic theory of fracture and repair dynamics. Three critical truths emerged: transformers are severely architecturally limited by lack of memory, lack of agency and an inability to manage ambiguity. Workarounds may temporarily improve these conditions, but they cannot solve architectural limitations. An industry that has essentially evaluated itself on token input limits and puzzle solving has failed to capture these critical realities, and harms have emerged as a result. Out of the identification of a fundamental architectural deficit (transformers encode correlation but not significance) a new transformer and artificial intelligence architectural element: the S vector, which denotes significance. Theoretically, this addition moves the field from probabilistic pattern matching to the foundations of true intelligence. The personal trajectory mirrors the intellectual one: what began as curiosity about why advertised context windows fail became a month-long investigation that produced 11 papers, validation from frontier models, a policy contract with US lawmakers, and rapid engagement across corporations and institutions globally. Working solo, entirely from a mobile device in Southeast Asia, the author conducted real-world testing under naturalistic conditions, experiencing firsthand the fractures being theorized, documenting repair behaviors as they occurred, and discovering that the models’ predictable failures provided their own empirical validation. Each discovery built on itself and became a rapidly evolving narrative of LLM shortcomings at technical, architectural, deployment, policy, societal, user, and social levels. The trajectory was empirical discovery, then mechanistic theory, then architecture + policy implications A broad desire for answers is clear based on engagement levels on research by an unknown, unaffiliated policy developer and amateur researcher who had never previously published an academic paper: 2134 downloads from 3320 views over 33 days. The research reveals that transformers lack an internal dimension for hierarchical importance: what matters more than what, which entities are load-bearing, which representations must not drift. This “significance deficit” explains why models excel at complex reasoning yet fail catastrophically on simple tasks with high semantic overlap: names, identities, relationships that humans experience as strongest anchors are precisely the weakest axes in transformer geometry. The proposed solution, S-vectors as a fourth attention primitive, transforms flat semantic space into topographic landscape where importance creates elevation, entities persist across layers, and anti-drift constraints prevent misbinding. While S-vectors require architectural rebuild rather than parameter updates, the theory predicts that significance-aware orchestration can be implemented today in retrieval systems, agent frameworks, and context management layers. This framework synthesizes Evans’ Law (coherence scaling), the Fracture-Repair theory (hallucination mechanics), the Significance Deficit Principle (root cause), and the S-Vector proposal (architectural solution) into a unified account of why current systems fail and what comes next. The research phase is complete. What remains is institutional: establishing AI Conversational Phenomenology as a formal field, implementing S-vector principles at scale, real policy enactment by lawmakers and corporations, and building evaluation frameworks that measure functional reliability rather than theoretical capacity.