By Jordan Meadows
Staff Writer
Artificial intelligence is rapidly reshaping the way we communicate, influencing not only the tools we use but also the structure, tone, and rhythm of modern language. As these systems become more embedded in everyday life, they quietly redefine ideas of clarity, efficiency, and correctness in written English, raising questions about authorship, authenticity, and the future of language itself.
The paragraph you just read was written entirely by artificial intelligence. Did you notice? If not, how could you have? What would you even be looking for?
As AI tools have become woven into everyday life, the challenge of recognizing when a piece of writing or speech is shaped by a machine has grown more complicated.
Certain AI tools leaned heavily on lists and corporate-style concision, relying on bullet points and structure instead of the flexible rhythm typical of human writing. Sentences often sounded oddly polished yet slightly off, built from grammatically perfect wording that didn’t quite match how people normally talk.
Repetition of sentence patterns and an over-reliance on familiar linguistic templates further hinted at automated origins. Buzzwords and jargon frequently crept in, especially in contexts where the system lacked deeper knowledge. Even immaculate grammar could be a giveaway, since real writers occasionally bend or break rules to emphasize an idea. Some models also showed consistent habits, such as favoring terms like commitment or perseverance, and sprinkling em dashes everywhere.
What complicates the picture today is that these patterns no longer belong to AI alone. Research from Florida State University shows that the language used by tools like ChatGPT has begun to seep into everyday speech.
This “seep-in effect” emerged after researchers analyzed more than twenty-two million words from unscripted podcast conversations before and after 2022. They found that certain terms favored by AI systems—words such as surpass, boast, and strategically—appeared far more often in spoken English after the release of ChatGPT, while similar synonyms remained steady.
The study revealed that people weren’t simply using new vocabulary because of cultural events, as happened with terms like Omicron during the pandemic. Instead, they were echoing the very language patterns introduced to them by AI tools. In academic and educational contexts, words like delve and intricate climbed in frequency, and the trend continued with others such as garner.
Nearly three-quarters of the tracked words increased in usage, with some more than doubling. One of the clearest examples came from the word underscore, which rose sharply, while its synonym accentuate did not.
To understand this shift, researchers first prompted ChatGPT to polish a massive body of text—emails, essays, news stories—and then extracted the words it inserted most often, dubbing them “GPT words.” When they traced these terms across hundreds of thousands of YouTube videos and more than seven hundred thousand podcast episodes across multiple years, the influence of these words was unmistakable.
According to outside experts, tracking single-word frequency is the right approach at this moment in AI’s evolution, because it captures the earliest signals of technological influence. As future models improve and grow more varied, the linguistic fingerprints will likely become harder to detect, pushing researchers to study deeper patterns such as sentence design and modes of presenting ideas.
Most people turn to AI for practical tasks: seeking information, drafting text, and sorting through everyday decisions. About half of all messages involve asking for guidance, reflecting a growing reliance on AI as an advisor rather than just a tool for doing work. Task-oriented exchanges make up roughly 40%, while 11% center on personal expression or reflection. Only a little more than one-quarter of interactions are directly tied to work, even though 30% of consumer usage has some professional connection.
This hints at a broader trend: AI generates value in ways that traditional metrics like GDP do not fully capture, especially by helping people think, decide, and communicate more efficiently.
Other patterns paint a picture of how people relate to these tools. Ten percent of conversations revolve around teaching and learning, a sign that many trust AI as a source of knowledge despite ongoing issues with hallucinations. A smaller share touches on relationships and introspection, illustrating how humans increasingly invite AI into their emotional and interpersonal lives. A tiny fraction even consists of casual chatter—a reminder that despite the heavy discussion surrounding AI’s societal impact, sometimes people simply talk to it for fun.
The topics dominating nearly eighty percent of all conversations—practical guidance, writing help, and seeking information—show how these tools are beginning to replace traditional search engines by gathering, organizing, and synthesizing information in one place.
Taken together, these shifts show why recognizing AI-generated language has become so tricky. The stylistic markers that once made AI obvious are now circulating among millions of everyday speakers and writers.
Language is changing because AI has become a participant in the language system itself. As these patterns continue to spread, distinguishing between human and machine may become less about spotting mechanical quirks and more about understanding how both are influencing one another.
