Regression to the Mean Explains LLM Trends and the Quiet Decline of Novelty
The essay examines regression to the mean in the context of large language models. It argues that early hype often gives way to average
The essay examines regression to the mean in the context of large
language models. It argues that early hype often gives way to average
performance over time. The author discusses how initial breakthroughs
lose novelty. Data points illustrate the pattern of diminishing
returns. Industry expectations are contrasted with observed trends.
Future research directions are suggested to manage expectations. The
piece warns against over‑optimism in AI hype cycles. It concludes with
a call for measured assessment of new models.