The pattern that researcher *Foreign_Tonight_7584* describes should worry anyone who relies on academic writing to do their job. We share their concern: papers are increasingly being written for machine readers, not human ones, and the consequences are practical, not philosophical. When introductions open with undefined terms like "trajectory parameterization" that only make sense after reading the entire paper, the document has failed its primary purpose. A paper that requires an AI to translate it before a human can understand it has outsourced comprehension itself.
This is not a style preference. It is a structural failure in how research gets evaluated and published. The reviewer describes a feedback loop that we find entirely plausible: AI-assisted reviewing rewards dense, jargon-heavy writing because models can process it easily, accepted papers become training data for new models, and those models then generate more of the same opaque prose. The result is a literature that looks sophisticated to a machine but is actively hostile to the person sitting down to read it from page one. For anyone who relies on staying current in their field, whether you are a data analyst, a product manager, or a researcher yourself, this means the time you spend decoding papers will only increase. The work of understanding is being shifted onto you, while the authors and reviewers offload theirs to AI tools.
What makes this particularly insidious is that the problem is invisible to the people creating it. The authors genuinely believe their terms are precise and efficient. To someone who has lived inside the work for months, "trajectory parameterization" is a useful shorthand. To a model with the full paper in its context window, it is equally clear. But to a human encountering the term cold, it is noise. The old safeguard against this was the human reviewer who read from page one and flagged undefined language. If that reviewer is now skimming an AI-generated summary, the safeguard is gone.
The practical takeaway is direct and testable. Before you submit your next paper, give an AI only the first page and ask it to summarize what the paper does. If the model has to guess, a human reader cannot follow either. And if you are reviewing, read the introduction yourself before touching any tool. When you encounter undefined, paper-specific terms stacked in the first paragraph, call it out in your review. That is not a nitpick about style. It is a failure to communicate, and it is the exact point where the loop can be broken.