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Something corrosive has spread through writing: the easy production of words without care. Call it the enshittification of writing. AI tools did not create this problem, but they made it cheap to scale the same shallow work that used to require human effort.
AI itself is not the enemy. Writing with AI is fine when used intelligently, with judgment, verification, and accountability how I use AI with judgment. The real issue is misuse: handing the machine the pen and pretending supervision is optional. When people treat AI as a shortcut to replace reporting, sourcing, and thinking, what follows is churn that looks polished on the surface and brittle underneath.
You see the failure modes everywhere. Hallucinations, where models invent facts, quotes, or citations that do not exist. Plagiarism by remix, where outputs repurpose others’ structures or methods without clear attribution. Surface-level amplification, where content mills crank out thousands of barely varied posts that repeat the same ideas. These products crowd the web with material that reads like explanation but lacks experience.
Editors and researchers are documenting this pattern. Publishers that leaned too hard on AI have had to correct and retract pieces after readers caught errors and unattributed passages, which proves speed without oversight breaks credibility. In academia, AI-generated manuscripts have been flagged for reusing others’ methods without citation, raising hard questions about credit and novelty. SEO practitioners warn that obvious AI patterns, including repetition, shallow coverage, awkward phrasing, and keyword stuffing, flood search results and push down genuinely original work. Detection and plagiarism tools help, but they are imperfect and reactive rather than preventive.
When Editing Tools Become the Problem
Tools like Grammarly and ProWritingAid are useful when they catch typos, tighten sentences, or flag unclear phrasing. Misused, they accelerate enshittification. Push every sentence through automated “clarity,” “engagement,” and “readability” sliders and you sand off voice, flatten rhythm, and convert lived language into corporate mush. Over-reliance nudges writers toward safe synonyms, generic transitions, and passive constructions that score well in a dashboard but read like beige paste on the page. These apps are assistants, not auteurs. Great for mechanical cleanup, terrible as final editors of style, argument, or truth.
Autocrit takes the same problem and dials it up. Its reports tempt writers to chase “ideal” averages for pacing, repetition, and sentence mix until a living draft is hammered into template compliance. The result often reads like it was written to satisfy a spreadsheet: technically tidy, emotionally vacant, and indistinguishable from a thousand other drafts that hit the same metrics. Used sparingly, these tools can highlight issues worth a human decision. Used as the decider, they train writers out of voice and readers out of patience.
Why This Matters
This matters because writing is how we share knowledge and make arguments. When content becomes churn, readers lose confidence. Brands that publish sloppy material sacrifice reputation. Research polluted by automated outputs steals attention and credit from the humans who did the labor of discovery.
What the Damage Looks Like
The pattern plays out the same way across industries. A team under deadline pressure leans on AI to generate drafts, routes everything through grammar dashboards, and uses automated scoring as the final gate. Output looks successful at first. Traffic rises. Editorial calendars stay full. Then a reader tries to verify a citation and discovers the DOIs do not resolve and the cited journal issues do not exist. What began as a comment thread becomes a credibility problem.
The correction is usually quiet. Remove the bogus citations, edit the copy, skip the editor’s note. That choice costs more than expected. Conversion signals drop. Reliability questions spike in support queues and on social channels. Internally, the editor and writers spend days re-checking sources and patching links, erasing any short-term savings from automating verification. The reputational hit is not a headline. It is a slow leak: partners and prospects circulate the discovery in private messages, and the lead generation funnel stiffens until trust is rebuilt.
This pattern matches documented problems in academic publishing. Predatory and low-quality journals have published AI-generated or misattributed articles containing fabricated references and DOI claims, forcing researchers and editors to police and retract content. The GIJIR investigation documented how an entire journal was populated with AI-generated articles falsely attributed to real researchers (Research Integrity and Peer Review). Neurosurgical Review retracted 129 papers after getting inundated by AI-generated commentaries and letters, most affiliated with a single university engaging in aggressive citation stacking (Retraction Watch). AI-written preprints with fabricated references have been withdrawn only to resurface under review at other journals (Retraction Watch). The broader context is a rise in retractions and integrity failures tied to automation and low editorial vigilance (Nature).
The Fixes
The remedy is practical and editorial. Keep humans central. Use AI for tasks it performs well, like brainstorming, outlining, or formatting, and never as a substitute for reporting, verification, or judgment. Require transparent attribution when AI plays a substantive role and document the verification steps taken. Train editors to spot AI fingerprints: repetitive phrasing, odd transitions, and unverifiable sources. Invest in fact-checking and source verification instead of chasing output quotas.
For academic work, peer review and novelty checks must adapt. Any AI-assisted manuscript should include provenance for ideas and explicit citations that can be verified. If methods overlap with prior work, reviewers should demand clear acknowledgment and treat uncredited idea reuse as a serious integrity issue.
Publishers and platforms should stop rewarding churn. If ad revenue and SEO tactics fund low-value content, the incentive to produce it stays in place. Cut the reward by prioritizing original reporting and verifiable data, require named authorship, and favor work that includes firsthand examples, interviews, or proprietary analysis.
Writers must use AI as a tool, not an author. Add first-hand reporting, original examples, a distinct voice, and take responsibility for every claim you publish. If your name is on a piece, own the facts and the argument personally.
Three practical steps stop the damage when it starts. First, require a verifiable primary source for every specific statistic or named study and log a timestamped verification step before publishing. Second, cut output targets so editors have time to do the work that automation cannot: calling sources, reading original papers, and confirming quotes. Third, publish transparent correction notices for substantive edits. Quiet fixes look like coverups and widen the trust gap.
There is a clear lesson here: treating writing as a metric to be optimized will buy volume but not trust. The inverse is also true. Paying the small upfront cost of verification preserves both reputation and long-term funnel performance.
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