AI labs are intentionally reducing the world knowledge embedded in language models to lower per-token compute costs. Despite having less built-in general knowledge, these models are simultaneously achieving higher reasoning scores. This deliberate trade-off prioritizes efficient problem-solving over broad memorization to create more optimized AI systems.