Photo by SPACEDEZERT on Unsplash Brain Rot, Feedback Loops, and the Shared Costs of Social Media Optimization In last month’s post, I introduced the idea of linguistic feedback loops in large language models (LLMs) through small but telling examples—words like delve that appear with surprising frequency in AI-generated text. These indicators of generative AI use emerge because they are statistically overrepresented in training data, and LLMs tend to reproduce what they encounter most often. A feedback loop forms when humans, through repeated exposure, begin to adopt these same patterns and use them more frequently in online content. That content then feeds back into future training sets, reinforcing the cycle. The “delve” phenomenon reflects a feedback loop that begins inside model training and spills outward into human use. This month, I want to look at a different (and potentially more consequential) dynamic: one that does not originate in models at all, but takes shape within human–social media platform ecosystems before being folded back into future training, with far more troubling implications. That dynamic has already been named. The …