The'Great Meme Reset' Is Coming From Jack Dorsey to Gen Alpha, everyone seemingly wants to go back to the internet of a decade ago. But is it possible to reverse AI slop and brain rot? Memes are getting a reboot. The Great Meme Reset of 2026, as it's being called on TikTok, demands that on January 1 all memes revert to their 2010s glory days. Bland " brain rot " and AI -looking memes are out; Big Chungus is in.

Is "Six Seven" Really Brain Rot? The viral phrase is easy to dismiss, but its ubiquity suggests something crucial about human nature. Recently, my wife was texting with a friend who lives in Singapore. The news from the other side of the world turned out to be that kids there had discovered "six seven." On Halloween, our friend reported, a boy with a handmade "six seven" jersey had earned applause as he made his way through her neighborhood--a place that's a long way from Sixty-seventh Street in Philadelphia, which the rapper Skrilla may have been referencing in his song "Doot Doot (6 7)," which came out last December.

A new study shows that feeding large language models low-quality, high-engagement content from social media lowers their cognitive abilities. AI models may be a bit like humans, after all. A new study from the University of Texas at Austin, Texas A&M, and Purdue University shows that large language models fed a diet of popular but low-quality social media content experience a kind of "brain rot" that may be familiar to anyone who has spent too long doomscrolling on X or TikTok. We live in an age where information grows faster than attention spans--and much of it is engineered to capture clicks, not convey truth or depth," says Junyuan Hong, an incoming assistant professor at the National University of Singapore who worked on the study as a graduate student at UT Austin. "We wondered: What happens when AIs are trained on the same stuff?"

We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To causally isolate data quality, we run controlled experiments on real Twitter/X corpora, constructing junk and reversely controlled datasets via two orthogonal operationalizations: M1 (engagement degree) and M2 (semantic quality), with matched token scale and training operations across conditions. Contrary to the control group, continual pre-training of 4 LLMs on the junk dataset causes non-trivial declines (Hedges' $g>0.3$) on reasoning, long-context understanding, safety, and inflating "dark traits" (e.g., psychopathy, narcissism). The gradual mixtures of junk and control datasets also yield dose-response cognition decay: for example, under M1, ARC-Challenge with Chain Of Thoughts drops $74.9 \rightarrow 57.2$ and RULER-CWE $84.4 \rightarrow 52.3$ as junk ratio rises from $0\%$ to $100\%$. Error forensics reveal several key insights. First, we identify thought-skipping as the primary lesion: models increasingly truncate or skip reasoning chains, explaining most of the error growth. Second, partial but incomplete healing is observed: scaling instruction tuning and clean data pre-training improve the declined cognition yet cannot restore baseline capability, suggesting persistent representational drift rather than format mismatch. Finally, we discover that the popularity, a non-semantic metric, of a tweet is a better indicator of the Brain Rot effect than the length in M1. Together, the results provide significant, multi-perspective evidence that data quality is a causal driver of LLM capability decay, reframing curation for continual pretraining as a \textit{training-time safety} problem and motivating routine "cognitive health checks" for deployed LLMs.

When one of Tim's year 8 pupils asked him about his "favourite Italian brain rot animal", he thought he'd misheard. "My hearing is not great at the best of times – I had to ask her to repeat this probably four or five times," he says. Tim (not his real name) was familiar with the term "brain rot", used to describe the sense of mental decline after too much time spent mindlessly scrolling online (and voted Oxford University Press's word of the year for 2024). But what was this about it being Italian? He told his pupil to get on with her work, sat down at his laptop – and immediately turned to Google.

George Orwell once wrote that every generation "imagines itself to be more intelligent than the one that went before it, and wiser than the one that comes after it". Today, the second part of that observation feels more astute than ever, as we face constant concerns about the ways modern technology is supposedly destroying the minds and cognitive abilities of children and young people. For decades, scientists have noted the occurrence of the Flynn effect, which essentially describes how, in terms of performance on various tests, each generation is more intelligent than the previous one.