In internet culture, the term "brain rot" describes the negative impact of consuming vast amounts of low-quality online content—particularly from social media—on human cognition. Research shows that excessive internet use can impair attention span, disrupt memory processes, and alter social cognition, including self-perception and self-esteem. As people spend more time online, their mental frameworks become increasingly shaped by fleeting, sensational, or emotionally charged content. Now, consider large language models (LLMs). These systems are trained on massive datasets drawn from the internet—text from websites, forums, social media, news articles, and more. While they don’t possess brains or neurons, they do have parameters and attention mechanisms that function in ways analogous to cognitive processes. Just as humans can become mentally overwhelmed or distorted by poor-quality input, LLMs may suffer from a kind of digital "cognitive decline" when trained on the worst parts of the web. The problem lies in the nature of the data. The internet is rife with misinformation, toxic rhetoric, logical fallacies, emotionally manipulative language, and repetitive, low-signal content. When LLMs ingest this material at scale, they risk internalizing these patterns. Over time, this can lead to outputs that are not only inaccurate or biased but also increasingly incoherent, hyperbolic, or prone to hallucination. In effect, the model’s "mind" becomes cluttered with noise. This phenomenon is not just theoretical. Studies have shown that models trained on unfiltered internet data are more likely to generate toxic, offensive, or nonsensical responses. They may also struggle with logical consistency, factual accuracy, and nuanced reasoning—hallmarks of cognitive degradation in human learners exposed to poor-quality information. Moreover, the feedback loop is dangerous. As LLMs generate content that gets published online, that content can be scraped and retrained on, further entrenching flawed patterns. In this way, the model’s training data becomes self-reinforcing, amplifying the very biases and distortions it originally absorbed. The solution isn’t to eliminate internet data entirely—much of it is valuable—but to critically curate and filter training inputs. Just as individuals benefit from digital detoxes and mindful consumption, LLMs need "data hygiene." This means prioritizing high-quality, fact-checked, and ethically sourced information, and actively excluding content known to be harmful, misleading, or low in signal. In the end, the principle remains the same: your model is what it eats. If we want AI systems that are intelligent, reliable, and safe, we must unplug them from the worst of the internet and feed them with the kind of data that fosters clarity, truth, and reason. Otherwise, we risk not just building flawed models—but perpetuating a cycle of digital decay that mirrors the very brain rot we seek to avoid.