How Generative AI Is Changing Our Vocabulary—And Maybe Our Thinking.

When I worked on one of the early posts in this series, a data scientist I was collaborating with pointed out a pattern in Generative AI writing that I couldn’t get out of my head. Certain words show up again and again in model-generated text. “Delve” is the one that hooked me.

Words like “delve,” “underscore,” and “intricate” have become a kind of AI accent. Small but reliable signals that a machine had a hand in the draft, at least for people who pay attention to these things.

Once I was alerted to it, I couldn’t stop seeing it. Delve kept jumping out at me, and I felt a small, smug satisfaction each time I noticed it. “Ah ha! You used ChatGPT…” It was like spotting a hidden watermark.

The irony is that I noticed all this because of my own writing. I had asked ChatGPT to tighten a paragraph in a draft, and the model quietly slipped in the word “delve.” On reviewing the post I was writing for technical accuracy, Jesse (the data scientist) caught this immediately and called it out as the classic AI fingerprint.

As an aside, I felt a kind of moral squeamishness about the use of generative AI here, as if I were cutting a corner I shouldn’t—like being caught smuggling notes into an exam. I find this instinctive reaction interesting, especially since the whole point of these tools is productivity, and their value ostensibly lies in speeding up the scaffolding around the thinking rather than replacing the thinking itself. In that light, my hesitation feels almost old-fashioned, maybe even a little generational. I’m interested to know what others think.

Anyway, as my delve obsession grew, I started seeing these fingerprints at work in the podcasts and articles of journalists and writers I have followed for years—people who have spent decades honing their voices. Were they now using generative AI? The emerging evidence suggests the answer is no. 

Linguistic Feedback Loops

Many authors, scholars and journalists are beginning to acknowledge that we are watching a linguistic feedback loop form: the stylistic traits overproduced by generative models circulate through the texts we read, are adopted into our own speech and writing, and then re-enter future training data—further amplifying those same patterns.

This is not unusual in itself; technological change has always shaped language, and language patterns shift in response to the communicative technologies and social environments that shape our interactions. However, there are many novel factors about the output of generative AI that are somewhat concerning in this context, and they have implications for creativity and broader cognition.

How AI Acquired Its Accent

First of all, the giveaway words and phrases in LLM output are not random. They come from specific sources, and the backstory is quite interesting. Early models like GPT-3.5 and GPT-4 were trained on massive datasets scraped from the open web, including mountains of material from freelance platforms and content farms. These services churned out blog posts, SEO pieces, marketing copy, product descriptions, and anything else companies needed at scale. Because hiring writers in the U.S, the U.K, or Europe was expensive, much of this work went to highly educated, low-cost freelancers in countries such as India, Kenya, the Philippines, Bangladesh, and across Eastern Europe. The freelancers’ English was strong, but they wrote under rigid style guides and optimized everything for search engines. The result was polished, formulaic prose packed with buzzwords and smooth transitions. When LLMs absorbed this material, they absorbed the patterns too, which is why their default voice often sounds clean and professional but also generic and short on texture, originality, and nuance.

As a result, the output of large language models now often defaults toward a predictable, over-polished style that favors clarity and balance at the expense of the natural shifts in tone and phrasing that gives human expression its personality. And research is already seeing signs of this bleeding back into the way we communicate. A 2024 study by Yakura et al. analyzed hundreds of thousands of hours of podcasts and YouTube videos and found measurable spikes in LLM-associated words in human speech after the release of ChatGPT. It seems a generative AI linguistic drift had already begun in 2024, and ChatGPT has more than doubled its user numbers since then.

The emerging research gives us an early indication of how language may be shifting in response to generative AI. The Yakura et al. study is striking because the linguistic influence they identify does not appear only in scripted or AI-assisted text. It shows up in relatively spontaneous speech—in podcasts, lectures, and science and tech conversations—suggesting that people may be absorbing and reusing the phrasing patterns they encounter in AI-generated text even when speaking extemporaneously. In other words, AI is beginning to participate in shaping the shared pool of expression we draw from.

Creativity, Effort, and the Risk of Flattened Expression

Wiederhold’s work helps explain why this might be happening. She notes that humans tend to follow the principle of least effort: we adopt the language that feels ready-to-hand, especially when it comes from tools we treat as authoritative. But creativity depends on deviation—those unexpected turns of phrase that break from the statistical average. Predictive models, by design, smooth out those deviations. They favor the safe, the familiar, and the stylistically “balanced.” If their output becomes the default texture of the language around us, we risk a narrowing of expressive range over time.

The Yakura study raises a related concern: cultural and epistemic homogenization. If LLM-shaped phrasing spreads widely, it may subtly shift what feels natural or reasonable to say. That matters because language is not neutral—it frames how we describe problems, articulate disagreement, and imagine alternatives. Even people who never use AI tools may find themselves influenced indirectly, simply by absorbing the linguistic patterns that others repeat.

Language Shapes Thought—So What Happens Next?

None of this is definitive, and the current research has limits. But the direction of travel is important. If human and machine language are becoming intertwined, then we should be much more intentional and strategic about what we optimize for—whether we want linguistic environments that cultivate diversity, friction, and originality, or ones quietly shaped by the statistical preferences of our tools, which seem to systematically favor the typical over the distinctive and produce fluent but fundamentally bland expression. 

After all, what is at stake is not simply vocabulary: the speech patterns we normalize shape our ideas, our relationships, the social cues through which we understand one another, and the wider epistemic environment in which knowledge is formed. So perhaps the simplest discipline is this: to notice. To notice what we absorb, what we echo, and whether those patterns reflect our own thinking or the gravitational pull of the machine. I, for one, have never used the word delve since…