The pursuit of Artificial General Intelligence (AGI) currently faces a fundamental data ceiling. While Large Language Models (LLMs) simulate reasoning by predicting text based on static, historical datasets, they lack the biological friction, physiological stress, and Conative mind (the will to act) required for true heuristic learning. This paper proposes an empirical paradigm shift: substituting synthetic, LLM-generated data with the human cognitive footprint captured across a diverse spectrum of video game genres. By utilizing a Triad Data Architecture to capture multi-genre telemetry—from the acute panic of Survival Horror to the long-term resource management of Strategy games—we mathematically map and digitize the bidirectional bridge between the virtual and real worlds. This cross-genre methodology provides the holistic, ground-truth training telemetry required to accelerate AGI foundation.
Krishna Soni (Sat,) studied this question.