<div dir="ltr">On Fri, 26 Jun 2026 at 19:59, spike jones via extropy-chat <<a href="mailto:extropy-chat@lists.extropy.org">extropy-chat@lists.extropy.org</a>> wrote:<br>><br>> Ja, and I really miss OK. Everything is awesome and excellent and perfect.<br>> Drives me nuts. On the phone with the medics, they ask questions, I answer,<br>> everything is perfect or awesome. I want OK back.<br>><br>> Ja. We also lost bad. Anything less than OK sucks now. That one I don't<br>> understand, for that term can be a good thing. The original meaning was all<br>> good. I don't understand how a term could turn thru pi radians. I want<br>> good, bad and OK back.<br>><br>> spike<br><div>> _______________________________________________</div><div><br></div><div><br></div><div>There are also the changes due to the widespread use of AI</div><div>People use AI to edit their writing and even start to talk like AIs.</div><div>BillK</div><div><br></div><div>iAsk AI -</div><div><div style="font-family:arial,sans-serif;font-size:small;color:rgb(0,0,0)" class="gmail_default"></div></div><div><div class="gmail-w-full gmail-flex gmail-items-center gmail-justify-end gmail-gap-x-2 gmail-sm:gap-x-1 gmail-relative gmail-pb-4 gmail-sm:pb-6"><div class="gmail-md:relative">
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<h3>The Linguistic Influence of Large Language Models</h3><p>The
integration of Large Language Models (LLMs) into daily life has
initiated a measurable shift in human communication, creating a "closed
cultural feedback loop" where the patterns generated by AI are
increasingly reflected in human speech and writing. Researchers have observed that specific vocabulary favored by
models—such as "delve," "meticulous," "pivotal," and "intricate"—has
surged in frequency across both academic papers and casual, unscripted
podcasts since the public release of ChatGPT. This phenomenon suggests that humans are not merely using AI as a tool,
but are subconsciously adopting its stylistic cadence, transition
phrases, and grandiose adverbs, effectively training their own
communication patterns on the output of the machines.</p><h3>Mechanisms of Linguistic and Cognitive Drift</h3><p>The transition
toward "LLM-speak" is driven by several factors, including the
convenience of productivity tools and the rapid diffusion of technology
into professional and creative workflows. In academia, studies have quantified this shift, finding that nearly
17.5% of computer science papers and a significant portion of peer
review texts contain content drafted or modified by AI. This adoption is often motivated by the need for speed and the pressure
to manage high volumes of communication, which leads users to rely on
the model's structural templates. Beyond simple vocabulary, users report a change in their own cognitive
processes, noting that the "rhythm" of AI-generated text—often
characterized by specific transition structures and a preference for em
dashes—begins to influence their original, unassisted writing. This creates a form of "cognition leakage," where the logic and style
of the model outcompete original human thought, potentially flattening
the diversity of human expression as both humans and AIs continue to
train on a narrowing subset of increasingly homogenized data.</p></div><div style="font-family:arial,sans-serif;font-size:small;color:rgb(0,0,0)" class="gmail_default">---------------------------</div><br></div></div>