There's no corpus large enough (www.swiftcraft.io)

Recent discussions in the AI community highlight a significant limitation in current artificial intelligence systems: the absence of a mechanism akin to mortality that governs real evolutionary processes. Researchers argue that genuine intelligence—as seen in biological organisms—arises from the experience of life and death, where decisions have real consequences. In contrast, AI models, despite their impressive capabilities, primarily engage in statistical pattern recognition and lack the experiential depth that comes from facing failure and survival. The vast computational resources available today are insufficient to replicate the evolutionary pressures that have shaped human intelligence over millions of years. As a result, the current pursuit of AGI may be fundamentally flawed if it continues to focus solely on scaling existing models rather than addressing the absence of existential stakes. This perspective challenges the very foundation of the AI revolution, suggesting that without the "ultimate stakes" provided by mortality, AI systems will only simulate intelligence rather than truly understand or learn in a human-like way. The implication is profound: if intelligence requires genuine experiences of consequence, then developing systems that can similarly learn and adapt is not just a technical hurdle, but a conceptual one that may remain insurmountable with current methodologies. This reflection pushes the AI community to reconsider the pathways to creating truly intelligent systems and prompts a reevaluation of what constitutes meaningful progress in the field.