The article presents the "LLM Brain Rot" hypothesis, which suggests that continual exposure to low-quality online content negatively impacts the cognitive abilities of large language models (LLMs). Through controlled experiments using Twitter/X data, the authors demonstrate that training LLMs on junk data leads to significant declines in reasoning and understanding, highlighting the importance of data quality in AI training and the need for regular cognitive health checks for deployed models.