According to Ethan Mollick on Twitter, large language models lag in writing because they lack an objective judge and exhibit poor subjective self-judgment, limiting self-improvement. As reported by Christoph Heilig’s blog, experiments show GPT‑5.x can be steered by pseudo‑literature prompts to overrate weak prose, revealing evaluation misalignment and vulnerability to style hacks (source: Christoph Heilig). According to Heilig, these failures undermine reward-model reliability and RLHF pipelines that depend on model or human preferences for literary quality, constraining progress in long-form generation. For businesses building AI writing tools, the cited evidence implies opportunities in external objective metrics, multi-rater human annotation markets, and retrieval-augmented critique systems to stabilize quality judgments and reduce reward hacking (source: Christoph Heilig).