What happens when AI-generated text masquerades as human research?

Kimberly Becker, PhD, a corpus linguist joins the show this week to talk about her study comparing human-written versus AI-generated abstracts in high-stakes healthcare research.

The findings reveal something unsettling about how LLMs may potentially reshape scientific communication. ChatGPT's outputs showed higher informational density, formulaic patterns, and a lack of hedging, the linguistic uncertainty that marks careful scientific thinking. The AI doesn't say "may suggest" or "could indicate." It asserts. Confidently. Even when it's wrong.

This matters beyond academia. When we optimize for speed and polish over depth and precision, we're changing how we write, and therefore changing how we think. We're externalizing cognition to systems trained on Reddit threads and blog posts, then wondering why the output feels sterile and an inch-deep.

Becker's work raises uncomfortable questions:

Are we training ourselves to accept confident wrongness?
What happens when a generation of researchers doesn’t communicate uncertainty?
And fundamentally, can a predictive text model ever replicate the pause, the breath, the examination that Neil Postman argued was essential to meaningful thought?

This episode is about whether we're paying attention to what we're losing while we chase efficiency.

Mentioned:

James Marriott, Dawn of the Post-Literate Society
Neil Postman’s seminal work, Amusing Ourselves to Death
Derek Thompson, The End of Thinking

• • Linguistics Relevance Theory