According to God of Prompt, the grokking phenomenon explains why certain AI fine-tuning runs, after a period of stagnation, suddenly achieve significant improvements. This effect is being observed in reinforcement learning from human feedback (RLHF) where models internalize human preferences, in domain adaptation for specialized large language models (such as those for medical and legal industries), and in advanced reasoning models like o1 and DeepSeek-R1. Prolonged training experiments are intentionally leveraging grokking to unlock these breakthroughs at scale, offering notable business opportunities for enterprises seeking to improve AI model performance and reliability in high-stakes domains (source: God of Prompt, Twitter, Jan 6, 2026).