The stigma around AI in journalism may be easing, but trust is still fragile

I tend to write about AI from the perspective of the bleeding edge, looking at how journalists and media companies are using the technology to change the way they work, reach new audiences, and transform their organizations. But the reality is that there’s a stigma around using artificial intelligence in the journalism community. In conversations I have with working reporters and editors, there’s clearly still a lot of reluctance, if not outright disdain, for using AI in almost any part of their work.

Looking at recent coverage of journalists using AI, however, you might think some of that disdain is going away. The Wall Street Journal recently profiled how Fortune business editor Nick Lichtenberg uses AI to turbocharge his output, sometimes writing as many as seven stories in a single day. The same day, Wired highlighted how several prominent reporters—including independents like Alex Heath and Taylor Lorenz as well as The New York Times’ Kevin Roose—use AI in various editorial tasks, sometimes in the writing itself. 

With all this, it feels as if a kind of dam has burst, and I don’t think it’s a coincidence that it’s happening at the same time Claude Cowork—which brings incredibly powerful agentic AI to everyone—has transformed the AI landscape. (An interesting aside buried in all this coverage of journalists’ use of AI is that it appears Claude is rapidly becoming what the Mac became among media pros: the platform of choice for creatives who “know better.”)

A cautionary tale in copy and paste

However, if the relationship between journalists and AI has been warming up, it got splashed in the face with a cold bucket of water last week when The New York Times severed ties with a freelance writer who had submitted a book review that was at least partially AI-written. The review by Alex Preston, published in early January, included passages that were nearly identical to Christobel Kent’s review of the same book that was published in The Guardian months earlier.

Preston admitted he used AI to assist in writing his book review, saying that he had “made a serious mistake.” While the incident is certainly a wake-up call for the Times (and not necessarily the first one) about how it communicates its AI policy to freelancers, it’s also a flapping red flag for any newsroom tempted to allow more AI use in their operations. Suddenly, there’s an error that seems to justify all the rules against it. 

That’s why it’s important to confront this directly. The incident steers us back into the dark cave of AI scandals in media—from CNET’s bot-authored service journalism to the made-up book titles in the Chicago Sun-Times’ “summer reading list” last year. It threatens to undermine all the gains many journalists and newsrooms are achieving in productivity, content optimization, and more, and potentially encourages those just taking their first steps with AI to fall back on the easy, blanket rule of “just don’t use it.”

That’s why it’s important to look closely at how AI was used so we can better delineate between good and bad AI use. It’s easy to say there wasn’t enough “human in the loop” (an increasingly unhelpful term)—but where in the loop? With prompting, fact-checking, something else? The whole point of AI is to outsource some human decision-making to sophisticated machines, so rather than pointing out the obvious—that humans need to shape and monitor the process—it’s better to zero in on the specific decisions that AI was asked to make, and whether the human gave the right parameters and restrictions.

When you examine this closely, it definitely appears the answer is no. According to The Guardian story, the two reviews have eerily similar language—so close that it’s difficult to argue against outright plagiarism. Look at these two passages:

• Original review, published August 21, 2025: “most significantly a song of love to a country of contradictions, battered, war-torn, divided, misguided and miraculous: an Italy where life is costume and the performance of art, and where circuses spring up on wasteland.”
• Times review, published January 6, 2026: “populate what is ultimately a love song to a country of contradictions: battered, divided, misguided and miraculous. This is an Italy where life is performance, where circuses rise on wasteland.”

Looking at the dates and the unquestionable similarities, we can draw some conclusions. It’s obvious Preston directly or indirectly asked the AI to create text he intended to include in the piece, and not just based on his notes. Given that the two reviews were published four months apart (and considering the typically lengthy editing process at the Times, he likely submitted it much earlier), that’s almost certainly not enough time for the AI’s training data to be updated. Which means the AI tool he used was incorporating web search (aka RAG) to come up with the copy.

This was a mistake. Giving Preston the benefit of the doubt, he may not have deliberately told the AI he was using to synthesize other reviews of the book, and perhaps it grabbed The Guardian review on its own. But he certainly didn’t tell the AI not to do that, which would seem to be an essential part of your prompt if you want to avoid the very plagiarized text he ended up including.

From taboo to tool

It bears repeating: In many—if not most—cases, how you use AI matters more than whether or not you use it at all. That requires acquiring a thorough understanding of these tools’ abilities and pitfalls, being meticulous about the parameters of your prompts, and a willingness to adapt your process continually. It’s an ongoing process, and it needs guardrails—such as “always” and “never” commands to avoid specific problems and (human) fact-checking. Otherwise, you’re playing with a gun that could easily go off.

There are systemic safeguards beyond simple techniques. Whether you’re an independent writer or a full newsroom, it pays to have an AI policy. As a media AI trainer, I of course would encourage investing in training, but I think it’s still objectively a good idea. But most importantly, the trial-and-error that comes with figuring out the boundaries of “good AI” should be kept out of public view if you can avoid it. In the case of AI-assisted writing, developing your prompting and guardrails in a private sandbox is essential.

That may seem obvious, but part of the “magic” of AI is that it creates outputs that seem identical to human-created outputs that have gone through a rigorous process. To the untrained eye, the appearance of competence feels good enough. Unlocking AI’s potential as a partner in writing and journalism means not simply trusting the underlying process, but accepting your role to build it, test it, and adjust it as needed. The more journalists do that, the more the stigma will fade.