According to @godofprompt, achieving grokking in AI models—where a model transitions from memorization to generalization—depends on several critical factors: the use of weight decay (L2 regularization), data scarcity that pushes the model to discover true patterns, overparameterization to ensure sufficient capacity, prolonged training, and selecting the right optimizer, such as AdamW over SGD. Without these conditions, models tend to get stuck in memorization and fail to generalize, limiting their business value and practical applications in AI-driven analytics and automation (source: @godofprompt, Jan 6, 2026).