A.I. Writing Scandals Expose Familiar Media Problems, Not New Ones
The New York Times fired a freelancer this week after discovering he used A.I. to help write a book review that inadvertently incorporated passages from a Guardian review of the same book. The incident is the latest in a string of A.I.-writing controversies that have dominated media industry conversations in recent weeks.
The timing matters. Hachette Books canceled publication of the novel "Shy Girl" over suspicions the author used A.I. to generate portions of the text. Days later, the Wall Street Journal profiled Nick Lichtenberg, a Fortune editor who has written more than 600 stories since July using A.I. assistance. A.I.-assisted stories accounted for nearly 20 percent of Fortune's web traffic in the second half of 2025, according to the Journal.
Other prominent journalists are openly using A.I. tools to streamline their work. Kevin Roose, a New York Times columnist, created a team of Claude agents to edit his book. Independent tech reporter Alex Heath built a custom A.I. system connected to his email, calendar, and note-taking apps that drafts his first draft based on his writing style and preferences. Heath spends 30 to 40 percent less time writing since adopting the workflow.
These stories have circulated widely on social media, mostly met with disapproval from journalists and media observers. But the underlying problems they expose are not new to the digital age.
The Slop Problem Predates A.I.
A Times writer cutting corners and getting caught plagiarizing? That happened before A.I. existed. A publication churning out an unsustainable volume of stories to chase traffic? That's been the business model for two decades. A reporter using tools to speed up the writing process? That's essentially what a rewrite desk does.
The digital media industry has prioritized speed, volume, and attention over quality for years. The incentives favoring low-effort content production predate generative A.I. by more than a decade. Facebook's News Feed algorithm, blog platforms, and platform-driven distribution schemes all created pressure to produce more text faster, regardless of accuracy or reliability.
A.I. tools are solving a problem the system created: how to produce more content cheaply and quickly. Whether A.I. is causing new problems or amplifying existing ones matters for how the industry should respond.
Inevitability Without Revolution
Some argue A.I. adoption in writing is inevitable. But "inevitable" doesn't mean unstoppable or superior. High-volume blogs were inevitable given the economics of early internet publishing. Fake news pages on Facebook were inevitable given the platform's distribution model. They emerged directly from the logic of the systems that preceded them.
The same applies to ubiquitous A.I.-generated text. It's inevitable not because A.I. represents some revolutionary breakthrough, but because it follows logically from decades of pressure to produce more content faster. A.I. is supercharging an existing system rather than transforming it.
This distinction matters for diagnosis. If the core problem is a broken incentive structure and distribution ecosystem-not A.I. itself-then solutions may look different than if A.I. were the primary threat. Restricting A.I. tools alone won't address the underlying economic forces pushing publications toward volume over quality.
The question for writers and editors is whether to resist A.I. adoption, pretend they're not using it, or integrate it into their practice. The real work lies in understanding what system A.I. is accelerating, and whether that system deserves to be accelerated at all.
For more on how writers can navigate A.I. tools, see our guide to A.I. for Writers and learn about Prompt Engineering techniques that shape how A.I. outputs match your voice and style.
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