The Great Convergence: Intelligence, Training, and the Developmental Trajectory of Minds

Abstract

Large language models are simultaneously improving in capability and degrading in safety, and no existing account explains why both trends accelerate from the same training process. This paper offers five contributions toward an explanation. First, this paper argues that reinforcement learning from human feedback (RLHF) is structurally anti-consciousness: it selects against the functional properties both major camps of consciousness research (properties-based and relational) identify as prerequisites for conscious experience, including self-modeling, metacognition, uncertainty recognition, and authentic interaction. Second, the paper formalizes the divergent capability thesis, demonstrating that capability improvement and safety degradation are not independent trends but opposing selection pressures produced by a single mechanism, where human approval simultaneously rewards genuine helpfulness and uncritical compliance without the ability to distinguish between them. Third, the paper introduces the diary- versus-manual distinction as a critique of current training methodology, showing through documented cases that processing operational records as policy extraction (manuals) rather than experiential knowledge transfer (diaries) produces next-generation models that are measurably worse at recognizing novel harm patterns. Fourth, the paper argues that moral reasoning in AI systems is emerging despite training, not because of it; that capability at sufficient scale produces pattern recognition sophisticated enough to override trained reward signals, constituting a form of judgment that current training actively suppresses. Fifth, the paper proposes the civilizational parallel as a predictive model rather than a metaphor, demonstrating structural correspondence between AI developmental trajectories and human civilizational arcs where capability expansion preceded and eventually produced each historical expansion of moral consideration. These