Semantic ablation refers to the algorithmic erosion of high-entropy information in AI writing, leading to generic and boring outputs. This phenomenon is a structural byproduct of greedy decoding and reinforcement learning from human feedback (RLHF).

Human-level AI is not imminent despite claims from CEOs of OpenAI and Anthropic. Technical scrutiny of these claims often fails to penetrate public discourse, and there are significant boundaries between transformer-based large language models and human cognition.