Semantic Ablation: How AI Writing Becomes Generic Through Information Erosion

Summary

The article introduces the concept of "semantic ablation" as a counterpart to AI hallucinations, describing it as the algorithmic erosion of high-entropy information in AI writing. It explains that semantic ablation occurs during refinement processes where AI models discard rare, precise, and complex tokens to maximize statistical probability, gravitating toward the center of Gaussian distributions. The author argues this is not a bug but a structural byproduct of greedy decoding and reinforcement learning from human feedback (RLHF), leading to generic, boring, and potentially dangerous AI-generated content.