Large language models ease surface workload but also mute the neural and linguistic fingerprints that mark genuine learning.

Paul Rohan wrote an article titled LLMs Are Killing Your Writing Fingerprints (17 June 2025).   

« A hefty 206-page MIT paper’s findings are super concerning for the future of human creativity.

“LLM users consistently underperformed at neural, linguistic, and behavioral levels”

Relying only on EEG, text mining, and a cross-over session, the authors show that keeping some AI-free practice time protects memory circuits and encourages richer language even when a tool is later reintroduced. »

« Quoting Ability: LLM users failed to quote accurately, while Brain-only participants showed robust recall and quoting skills. »

« Critical Thinking: Brain-only participants cared more about 𝘸𝘩𝘢𝘵 and 𝘸𝘩𝘺 they wrote; LLM users focused on 𝘩𝘰𝘸. »

« Cognitive Debt: Repeated LLM use led to shallow content repetition and reduced critical engagement. This suggests a buildup of “cognitive debt”, deferring mental effort at the cost of long-term cognitive depth… Students who started with ChatGPT and later went tool-free kept the sluggish pattern, showing cognitive debt. By contrast, students who first wrote unaided and then tried ChatGPT lit up wide networks, suggesting that prior brain exercise keeps circuits ready even when automation enters the scene. »

« Takeaways: Large language models ease surface workload but also mute the neural and linguistic fingerprints that mark genuine learning. Regular tool-free practice keeps memory circuits active and sustains individual voice, making later automation a boost rather than a crutch. For anyone who writes with AI, the best insurance is to draft at least some pieces with nothing but the mind’s own search engine. »