Tap the headphones icon at the top of a New York Times or New Yorker story and you may hear it read by an automated voice. The feature has spread across major news organizations, as publishers use AI-generated narration to give readers another way to consume their journalism.

In a Columbia Journalism Review analysis, contributing writer Jem Bartholomew examines how The New York Times, The Washington Post, The Wall Street Journal, The New Yorker, The Atlantic and Vanity Fair have adopted automated text-to-speech tools. ElevenLabs, the AI voice company that partnered with The New Yorker in 2023, said this year that it had surpassed $500 million in annual recurring revenue. The company offers more than 10,000 artificial voices and has held early talks with investors about a secondary offering that would value it at $22 billion.

The technology is already drawing significant use. About 20% of New Yorker subscribers listen to narrated stories, deputy editorial director Monica Racic told CJR. At The Wall Street Journal, the “Read to Me” feature, developed through a partnership with Microsoft that began in 2020, was used about 5 million times over the past year and had a 65% completion rate.

New Yorker editor David Remnick told Semafor that an ElevenLabs voice sounded “pretty damn good” and was “nearly instantaneous” to produce. ElevenLabs CEO Mati Staniszewski told Al Jazeera in February that the company’s technology could produce speech based on the contextual understanding of written text.

But CJR’s Bartholomew found limits in the technology. AI narrators can rush through passages that call for pauses, emphasize unexpected words and use simulated breaths or rising intonation at unnatural moments. The result can sound convincingly human while still feeling artificial.

Bartholomew found the difference most striking when he compared an AI narration of a 1997 New Yorker profile of Donald Trump by Mark Singer with Singer’s own reading of the piece for the audiobook version of his 2016 book, Trump and Me. Singer died in June.

The synthetic voice maintained a steady pace through passages that depended on irony, sadness and changes in tone. Singer’s original reading conveyed those shifts; the AI version largely did not. For Bartholomew, the comparison showed what can be lost when a piece of writing shaped by a human writer is delivered by a synthetic voice.

Despite the loss of tone and nuance, accessibility is one of the clearest arguments for AI narration. A Times spokesperson told CJR that the newspaper has experimented with automated voices for years as a way to make its journalism more accessible, including for people who are blind or have low vision. ElevenLabs says its impact program provides free licenses to nonprofits and people with accessibility needs.

Publishers also see narration as an additional way to reach audiences rather than a replacement for written journalism. Taneth Evans, the Journal’s head of digital, told CJR that the feature is “additive” and gives readers another way to engage with the newspaper’s work.

The New Yorker has drawn a line around fiction. The magazine does not use AI to narrate its fiction, which is read by authors whenever possible because the reading is treated as a performance.

AI narration gives publishers another way to put journalism in front of audiences wherever they are. But as synthetic voices become more common, newsrooms face a more consequential question: How much of a story’s meaning survives when the human voice is taken out of the telling?

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USA Today Co. told investors Thursday it has signed Palantir to analyze and monetize how its readers behave, a bet the country’s largest newspaper chain is making as its search referrals plummet. The company owns USA Today and more than 200 local papers, and it reported 158 million unique visitors in the second quarter, down from 180 million in the first, according to Neiman Lab.

Chairman and CEO Mike Reed said the deal is aimed at turning more readers into paying customers. “Every visit, every session, and every moment of attention creates a signal,” he said. The goal, he added, is to turn USA Today’s large audience into direct, first-party relationships. In practice, that means converting anonymous readers into logged-in users whose behavior can be tracked and monetized.

Executives said the decline in traffic does not reflect weaker demand for USA Today’s journalism. Instead, Kristin Roberts, president of USA Today Media, attributed it to “lower referrals from traditional search because of those consumer discovery changes.”

Other publishers are reporting a similar trend. The New York Times said this week that it “isn’t immune” to the decline, as news organizations increasingly turn to video, subscriptions and direct audience relationships as search referrals weaken.

The choice of Palantir also brings scrutiny. The company has faced sustained criticism over its work with government and military clients, including Immigration and Customs Enforcement, the Pentagon and the Israeli military. Palantir co-founder and chairman Peter Thiel also bankrolled the lawsuit brought by Hulk Hogan that led to the bankruptcy of Gawker.

That history raises questions about the decision by a major news organization to use Palantir to analyze its audience data.

USA Today Co. is not the only news company using Palantir’s data intelligence tools. Axel Springer, which owns Business Insider, Politico and The Telegraph, and Fox News have also signed deals with the company. Reed sought to emphasize that USA Today Co. retains control of its data. “All of our data remains our data,” he said, describing the partnership as a way to “leverage this incredible AI and software” rather than hand the data over to Palantir.

USA Today Co. also said the partnership would not affect its editorial independence. The company pointed to its journalistic standards and ethics policies and said it complies with data protection laws while requiring the same of its vendors. Roberts told staff the deal could lead to “better recommendations and offers” and “smarter subscription and advertising experiences.”

For newsrooms, the deal reflects how publishers are adapting to the decline in search traffic. As Google sends fewer readers to news sites, publishers are putting more emphasis on direct relationships with their audiences and finding new ways to generate revenue from existing readers. That shift makes audience data increasingly valuable and could push more publishers toward partnerships with technology companies that specialize in analyzing and monetizing it.

Reed also suggested USA Today Co. could use its content as leverage in negotiations with Google. He said he could envision “a day where we turn off scraping” of USA Today content, although he would prefer to reach “a fair licensing deal” that keeps the company’s journalism in both traditional search results and AI-generated summaries. “But if we have to cut them off and block them in order to get to a deal, then we’ll do that for sure,” he said.

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The New York Times added 280,000 digital-only subscribers in the second quarter of 2026, down from the 310,000 it added in the first three months of the year. As search traffic declines, the company is increasingly turning to video to reach and retain audiences.

The slowdown came during a quarter packed with news, including the Iran war, the FIFA World Cup and four Pulitzer wins, Nieman Lab reported.

CEO Meredith Kopit Levien pointed to the changing economics of the web.

“We delivered our Q2 results against the backdrop of a rapidly changing information ecosystem shaped by a small number of big tech companies whose moves continue to result in less traffic to publishers,” she said in prepared remarks for the company’s earnings call. “The Times isn’t immune to that impact.”

That’s a notable admission from a publisher that has long been viewed as one of the best positioned to withstand the decline in search traffic.

Smaller news organizations have been warning about falling referrals from Google and other platforms for years. The Times has had a significant advantage: 13.3 million total subscribers and a direct relationship with readers that many publishers lack. The company says it remains roughly on track to reach 15 million subscribers by the end of 2027. Total subscription revenue rose to $538 million in the quarter.

But the Times is increasingly looking beyond search to reach audiences — and video is a big part of that strategy.

Kopit Levien described the company’s video investments as “long-term bets.” They helped push adjusted operating costs up 10% from a year earlier. The Times hired eight video journalists in January and currently lists 12 open positions focused on video.

The company added a Watch tab to its flagship app last year and launched a Shows tab this week for longer-form franchises spanning news, opinion, culture and lifestyle.

Much of that programming puts Times reporters in front of the camera to explain their own reporting. Kopit Levien called the format “inherently humanizing and trust building.” The company now produces thousands of videos each quarter.

Asked about the financial return from the video push, Kopit Levien said video has played a “minor role” in advertising revenue so far. The company’s priority, she said, is to “really focus on scaling production, scaling engagement, and then scaling monetization.”

The Times did not disclose engagement figures, making it difficult to assess how well the investment is working from the outside.

There’s another cost worth watching: the company’s legal fight over generative AI.

The Times spent $4.6 million on generative AI-related litigation during the quarter, bringing its total to $32.9 million since it began breaking out those costs in early 2024. For the country’s most successful digital news publisher, litigation over the use of journalism to train AI systems has become a recurring, eight-figure expense.

For other newsrooms, the Times’ results point to an uncomfortable reality that even scale may not be enough to offset the loss of search traffic.

The Media Copilot has tracked declining search referrals across the industry, and the Times’ experience suggests the problem is not limited to smaller publishers. Its response — building direct distribution through video on its own platforms — requires substantial investment and may take years to monetize.

That leaves smaller publishers looking for cheaper ways to accomplish the same thing.

The audience logic is straightforward. Pew Research Center data shows Americans increasingly getting news from video-first platforms, particularly younger audiences.

The bigger question is whether the Times can turn that audience into paying subscribers quickly enough to justify the cost of building a video operation — before the economics of digital publishing shift again.

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A translation of a mass shooter’s journal written in Faux Cyrillic. A scraper that pulled every public meeting minute from a Texas county website. Tens of thousands of leaked emails from a Chinese surveillance firm, made searchable with a large language model.

Each of those reporting efforts won or was a finalist for a Pulitzer Prize this year. And each relied, in some way, on AI.

Nieman Lab’s Andrew Deck reports that eight Pulitzer honorees disclosed AI use to the judging committee in 2026: five winners and three finalists. That’s the most since the Pulitzer Prizes began requiring AI disclosures in 2024—and it points to a shift in how journalists are using the technology.

In the two years before that, reporters mostly used older forms of machine learning — embedding models for data visualization, for example, or pattern-recognition tools to analyze satellite imagery. This year, commercial large language models did more of the heavy lifting, especially when reporters faced mountains of documents.

The approach was similar across this year’s Pulitzer winners. At The Wall Street Journal, computational journalist John West and his colleagues built a custom scraper to collect records from Kerr County, Texas, after deadly summer floods. They then used an internal tool called WSJPT to summarize every page and flag references to previous flooding events. Reporters still read each flagged section themselves.

“We aren’t obviating the need for human investigation of a pile of documents,” West said. “Instead, we’re trying to sort the pile so the most relevant stuff is right at the top.”

At The Minnesota Star Tribune, engineer Dana Chiueh used an enterprise ChatGPT account to analyze screenshots of a Minneapolis church shooter’s journal. The model identified the writing as Faux Cyrillic and produced a first-pass translation of more than 600,000 words.

The team then used Google’s NotebookLM to identify recurring themes and had two Russian-language academics at St. Olaf College verify key passages. Their review caught several errors, including one that mischaracterized the shooter’s motivation.

The reporting won the Pulitzer Prize for Breaking News.

The Associated Press used large language models to make tens of thousands of leaked documents searchable for its Pulitzer-winning investigation into American technology companies’ role in China’s surveillance state.

But reporters did not simply trust what the models surfaced. They manually reviewed documents flagged by AI, independently checked the accuracy of AI-generated summaries and did not quote from those summaries, said AP investigative journalist Garance Burke.

The New York Times flipped the usual workflow. For its investigation into the Securities and Exchange Commission’s retreat from crypto enforcement under the second Trump administration, reporters manually read and classified more than 10,000 documents. They then used OpenAI’s GPT-5 to conduct a second pass, flagging discrepancies for reporters to review.

In this case, AI was used to check the humans — not the other way around.

For newsrooms, perhaps the more revealing detail is what readers never saw.

The Wall Street Journal did not disclose its use of AI in the flood investigation at publication. West said the tools functioned essentially as a more sophisticated search system.

That gap between disclosing AI use to a prize committee and disclosing it to the public is at the center of an increasingly important debate over newsroom standards. It’s a tension that has surfaced repeatedly as news organizations figure out when AI use is significant enough to tell readers about.

Pulitzer administrator Marjorie Miller said the industry now has a clearer understanding of where AI can be used appropriately, such as data collection and analysis, and where its use raises more questions, including writing and editing.

Next year, after controversies over AI-generated text in prize-winning literary works, the Pulitzers will add an AI disclosure question to their book entry forms.

“AI is here to stay,” Miller said. The committee, she added, will continue asking entrants to demonstrate that a human produced the work.

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OpenAI is putting another $5 million and $3 million in technology credits behind the American Journalism Project over the next two years, executives told Axios in an exclusive report. The renewal extends a partnership that began in 2023, when the company committed $5 million in funds and another $5 million in credits to the nonprofit.

That original deal helped launch AJP’s Product & AI Studio, which helps nonprofit local outlets build AI-powered products and workflows. In its first run, AJP delivered direct grants to 31 of its 50-plus portfolio organizations across 38 states. With the new money, CEO Sarabeth Berman says AJP plans to fund more of its newsroom partners while broadening access to ChatGPT’s enterprise products.

Tom Rubin, OpenAI’s chief of intellectual property and content, framed the arrangement as mission-driven rather than commercial. “These partnerships are consistent with our mission and have demonstrated great success,” he told Axios. “We’re committed to them because they demonstrate that the technology can benefit society.”

Member newsrooms used the funding on things reporters and fundraisers deal with every day: donor communications, data analysis, translation and civic information tools for readers. The next stage moves past one-off experiments toward shared products and infrastructure that smaller newsrooms could use together, according to AJP.

AJP is one of several local news bets OpenAI has placed. The company runs an AI collaborative and fellowship with the Lenfest Institute for Journalism that backs metro publishers including the Philadelphia Inquirer, Minnesota Star Tribune and the Seattle Times. It funded an expansion of Axios Local into new markets in 2025, and it provides grants and training to newsrooms in the global trade group WAN-IFRA.

The timing matters because OpenAI is buying goodwill with publishers while fighting them in court. It faces copyright suits from the New York Times and from eight newspapers owned by Alden Global Capital, part of a broader wave of legal disputes over how AI systems use published work. Other firms have been slower to sign licensing deals, and some, including OpenAI backer Microsoft, are exploring models that would pay publishers per use instead.

For newsrooms weighing whether to take this kind of money, the opportunity comes with tradeoffs. Grants and credits lower the cost of experimenting with AI, but they come from a company that news organizations are also suing over training data. The tools that help a local outlet translate coverage or draft donor emails are built by the same industry accused of scraping journalism without permission.

“Deploying AI effectively is ultimately a leadership challenge,” Berman said. “News organizations really have to think about how they smartly integrate these technologies in ways that have good policies, have humans in the lead and support the journalistic quality of the news organizations.”

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The New York Times and a group of other publishers are asking a federal court to sanction OpenAI, accusing the company of withholding or destroying evidence in a high-stakes copyright case over how ChatGPT was trained and used.

In a motion filed Thursday in federal court in Manhattan, the publishers alleged that OpenAI misrepresented its ability to search training datasets and ChatGPT output logs for copyrighted news material. According to Reuters, the publishers said OpenAI told the court it could not search its large language models for their work while allegedly concealing that it had already done so “even before the first News Plaintiff filed suit.”

The motion is the latest escalation in the copyright fight between major news organizations and AI companies. It also moves the dispute deeper into discovery, where the question is not just whether AI companies can use journalism to train models, but whether they can preserve, search and produce the records needed to prove what happened.

The plaintiffs include The Times, the New York Daily News and other media organizations, including Ziff Davis and the Center for Investigative Reporting, according to The Associated Press and Variety. The original New York Times article reported that the publishers are seeking legal sanctions against OpenAI, including monetary penalties and other remedies.

The filing does not ask for sanctions against Microsoft, which is also a defendant in The Times’ broader copyright case, according to The Times’ summary of the motion. Microsoft has invested heavily in OpenAI and integrated OpenAI technology into products including Copilot.

“The evidence is in OpenAI’s training data sets and ChatGPT output logs,” the publishers said in the motion, according to The Times. “But instead of just producing that evidence at the start of the case and focusing on the merits of its fair use defense, OpenAI chose obstruction.”

OpenAI rejected the allegations. “As the Times’ case weakens and they’ve been forced to drop claims against us, they’re persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations,” OpenAI spokesperson Drew Pusateri told Reuters. “We’ll continue defending our users’ privacy and the long-established principles of fair use.”

The publishers allege that OpenAI deleted billions of relevant ChatGPT conversations or made them unsearchable. They also argue that an OpenAI employee later testified that the company had performed multiple searches for news publishers’ content, contradicting earlier representations about the company’s technical limitations.

A sanctions memorandum posted by Ars Technica says the publishers want the court to bar OpenAI from relying on a disputed 20 million-log ChatGPT sample, find that ChatGPT’s output logs include or would have shown substantial use of the publishers’ copyrighted material, instruct the jury on those findings and award fees and costs tied to the discovery fight.

Those remedies would matter because discovery disputes can shape the trial record. If the court finds OpenAI failed to preserve or produce relevant evidence, the ruling could affect what arguments OpenAI can make later and what conclusions a jury may be allowed to draw from missing or incomplete records.

The Times sued OpenAI and Microsoft in 2023, alleging that millions of Times articles were used without permission to train AI systems that now compete with publishers as sources of information. OpenAI and other AI companies have argued that training models on large bodies of text is protected by fair use, a theory now being tested across lawsuits from authors, artists, music labels and news organizations.

For publishers, the issue goes beyond training data. They argue that AI chatbots and AI search summaries can answer readers’ questions using journalism without sending traffic, licensing revenue or subscribers back to the organizations that reported the information. Media Copilot has been tracking the same pressure point in coverage of Google’s AI accuracy problem and The Times’ warnings about AI companies using journalism without permission.

At the same time, publishers are taking different approaches to the AI economy. Some are suing. Others have signed licensing deals with AI companies. The Associated Press announced a deal with OpenAI in 2023, while other media companies have made agreements with OpenAI, Google, Meta and Amazon.

The sanctions motion could increase pressure on both sides. A ruling against OpenAI would give publishers leverage in court and in licensing talks. A ruling for OpenAI would strengthen the company’s argument that publishers are using discovery to intrude into user privacy and commercially sensitive systems.

Either way, the case shows that AI copyright fights are becoming data-governance fights. The central questions are no longer only what AI systems were trained on. They are whether companies can prove it, search it, preserve it and explain it in court.

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A.G. Sulzberger, publisher of The New York Times, delivered a sharp rebuke to the artificial intelligence industry Monday, accusing major tech companies of building their AI products on “brazen theft” of journalism and calling on news organizations worldwide to push back before it’s too late.

In a speech at the WAN-IFRA World News Media Congress, Sulzberger argued that AI companies are systematically strip-mining news content without permission or compensation, hollowing out the very public square they claim to serve.

“Their hijacking of the public square is made possible by the original sin that animates their AI products — a brazen theft of intellectual property that has occurred at an unprecedented scale,” Sulzberger said in prepared remarks. “Tech giants strip-mine news websites without permission or compensation. They repackage these stolen goods as their own, siphoning off the audiences and revenue that otherwise would go to the news organizations that created this work.”

Sulzberger laid out what he called the four ingredients of AI: talent, compute, energy, and data. The first three are paid for — engineers earn tens or hundreds of millions, data centers cost hundreds of billions. But “data,” Sulzberger argued, is treated differently, seized without consent or compensation despite being equally essential.

The tech industry’s justifications — that innovation requires it, that facts can’t be owned, that “fair use” permits it, that licensing deals take too long — don’t hold up, Sulzberger said. He noted that five of the top 10 sites used to train leading language models belong to news publishers, and that OpenAI has acknowledged it would be “impossible to train today’s leading AI models without using copyrighted materials.”

The financial stakes are enormous. The six leading AI companies have a combined valuation of $11 trillion — more than three times the GDP of France. Private AI investment in the U.S. reached nearly $350 billion in 2025. Yet industry data suggests less than half of 1 percent of that investment goes to compensate the publishers whose content powers the technology.

The impact on news organizations is already measurable. The largest newspapers tracked by Comscore saw traffic drop more than 45 percent on average as the AI race intensified over the last four years. Meanwhile, Meta alone now makes eight times more in ad revenue than every newspaper on earth combined.

“The tech giants are fully aware of the implications of this shift,” Sulzberger said, quoting a Microsoft executive who wrote that “the open web was built on an implicit value exchange where publishers made content accessible, and distribution channels helped people find it. That model does not translate cleanly to an AI-first world.”

The Times publisher was careful to position his remarks not as anti-AI. He noted the Times uses AI internally — “responsibly, ethically, and with humans making the decisions” — to improve how it reports and distributes journalism. “Holding a powerful new technology at arms length is a recipe for failure,” he said.

But he pushed back hard on the idea that paying for content would cripple American competitiveness. “In its competition with China, America weakens itself by abandoning the intellectual property protections that fuel innovation and power America’s creative enterprises,” he said.

Sulzberger acknowledged the irony of a 175-year-old newspaper criticizing tech disrupters. But he argued the AI situation is different: the companies aren’t being disrupted by new technology — they’re the ones doing the disrupting, and they’re doing it on the backs of creators they’ve refused to compensate.

He urged the assembled news leaders from more than 60 countries to be more vocal. “Our profession has been too quiet, too passive and too fragmented in the face of abuses by the companies leading the AI revolution,” he said.

The speech ended with a plea for news organizations to stand firm on their value — and to stop pretending information wants to be free. “Information is valuable. Journalism is valuable,” Sulzberger said. “We cannot afford to be as naive this time.”

Edited by Pete Pachal

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My usual focus is the cutting edge of AI in media, examining 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 a persistent stigma still hangs over artificial intelligence in the journalism world. 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.

Recent media coverage, though, paints a different picture. 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.

Taken together, it feels like a dam has finally burst. And I don’t think the timing is accidental—this shift is happening alongside the arrival of Claude Code and Cowork, which has put remarkably powerful agentic AI within reach of everyone and reshaped what people expect from these tools. (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 plagiarism scandal puts AI trust on ice

But just as the relationship between journalists and AI seemed to be thawing, a high-profile incident threw it back into doubt. Last week, The New York Times severed its relationship 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.”

The episode is a clear wake-up call for the Times—and not its first—about communicating AI policy to freelancers. But it also sends a warning signal to every newsroom that has been inching toward greater AI adoption. Here, suddenly, was an error that appeared to validate all the restrictive rules.

Confronting what happened directly matters. 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 risks erasing the productivity and content optimization gains that many journalists and newsrooms have been making, and could push those just beginning to experiment with AI back toward the simplest possible rule: don’t use it at all.

That makes it essential to examine specifically how AI was deployed here, so we can draw a clearer line between responsible and irresponsible 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 look at the details, the answer is clearly no. According to The Guardian story, the two reviews have eerily similar language—so close that it’s difficult to argue against outright plagiarism. Consider these side-by-side 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.”

Given the dates and the undeniable overlap, a few things become clear. Preston evidently asked the AI—directly or indirectly—to generate text he planned to use in the piece, and not just from his own notes. The four-month gap between the two reviews (and likely an even longer lead time given the Times’ editing process) almost certainly means the AI’s training data didn’t include Kent’s review. That points to the AI tool pulling from web search (also known as RAG) to produce the copy.

This was the critical error. 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.

Moving from stigma to smart adoption

It bears repeating: in most cases, how you use AI matters far more than whether you use it. Getting there requires deep familiarity with these tools’ strengths and weaknesses, careful attention to prompt design, and a commitment to continuous adaptation. It’s an ongoing process, and it needs guardrails—such as “always” and “never” commands to avoid specific problems and (human) fact-checking. Without those safeguards, you’re handling a loaded weapon that can easily misfire.

Broader structural protections help, too. 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.

When it comes to AI-assisted writing specifically, developing your prompts and safeguards in a private sandbox is critical. That might seem obvious, but one of AI’s most deceptive qualities is that it produces outputs that look indistinguishable from work that went through a rigorous human process. To someone without experience, that surface-level competence feels sufficient.

Truly making AI work as a writing and journalism partner means going beyond trusting the process—it means accepting responsibility for building, testing, and refining that process yourself. The more journalists do that, the more the stigma will fade.

A version of this column appears in Fast Company. It has been lightly “remixed” (alternate words and phrasings used) with AI assistance and human review.

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The New York Times has cut ties with a freelancer after discovering he used AI to help write a book review that incorporated elements of a Guardian review on the same book.

A reader notified the Times in late March that its January 6 review of “Watching Over Her” by Jean-Baptiste Andrea bore similarities to a review the Guardian published in August 2025. The Times review was written by author and journalist Alex Preston. The Times launched an internal review and spoke with Preston, who admitted he used an AI tool to help draft the piece and failed to catch the Guardian material before publication, TheWrap reported.

“Editors have appended a note to a book review written earlier this year by a freelance critic, who told The Times after publication that he had used an A.I. tool to assist him in producing the piece,” a Times spokesperson said. “This tool produced similarities to a book review published in The Guardian, which our editors’ note makes clear. For staff journalists and freelance writers alike, reliance on A.I. and inclusion of unattributed work by another writer is a serious violation of the Times’s integrity and fundamental journalistic standards.”

Preston told TheWrap he used an “A.I. editing tool improperly on a draft I had written” and failed to catch “overlapping language” from the Guardian review. He said he has not used AI on his books or other published pieces. The Times notified the Guardian and added an editor’s note to the online review acknowledging the AI use and linking to the original Guardian piece. Preston, who has written six reviews for the Times between 2021 and 2026, will no longer write for the paper.

The incident comes as the Times has been vocal about its stance on AI transparency in journalism. The paper published internal principles stating that work using generative AI must be “vetted by our journalists” and “reviewed by editors,” and that articles should explain to readers how AI was used and the steps taken to “mitigate risks, such as bias or inaccuracy.” “The first principles of journalism should apply just as forcefully when machines are involved,” the Times said.

Preston is a six-time author whose most recent book, “A Stranger in Corfu,” was published last month by Pegasus Books. He has also published work in the Financial Times, the Economist, and the Guardian, and serves as head of advisory for the Man Group investment management firm.

The episode highlights the ongoing tension between newsrooms that are wrestling with AI adoption and the freelancers who contribute to them — a dynamic playing out as outlets like the Times navigate broader disruptions to the journalism industry.

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The New York Times is upping it’s AI game.

The company on Wednesday launched Crossplay, a two-player word game much like Scrabble and Words with Friends, alongside an AI-powered analysis tool called Cross Bot. The bot reviews completed matches and provides personalized feedback to help players improve their skills.

Cross Bot calculates strategy and luck scores for each game, highlighting three to four key turns that offer learning opportunities. It shows the best possible moves for every turn, ranked by both points and strategic value.

The tool goes beyond simple point calculations. It runs simulations to evaluate how each potential move might affect the next two turns, considering both what opponents could play and what options remain for the player.

“Some moves make it easier for your opponent to score a triple-word bonus,” the Times wrote in announcing the feature. “Other moves make it easier for you to have a high-scoring move in the next turn.”

The bot evaluates hundreds or thousands of possible moves per turn, narrowing them down based on points scored and which tiles remain in the player’s tray. Keeping a balanced mix of vowels and consonants rates higher than moves that leave difficult letter combinations.

A heat map feature shows “lanes” on the board where high-scoring plays are most likely. Brighter lanes indicate more opportunities for big points.

Cross Bot can analyze any completed game played against another human with at least five turns. Games against the computer are not eligible for review.

The launch represents a notable investment by the Times in AI-assisted features for its popular games portfolio. The company’s games division has become a significant driver of digital subscriptions, with Wordle and the crossword puzzle attracting millions of daily players.

Strategy scores range from 1 to 99, measuring how well a move sets up a player to win. The bot compares game states against outcomes from millions of previous Crossplay matches to generate these ratings.

Luck scores use the same scale, measuring how useful a player’s drawn tiles are compared to random alternatives.

The highest-scoring move is not always the best move, the Times noted. Board placement matters. Playing a lower-scoring word that blocks an opponent from accessing a triple-word bonus can be smarter than grabbing immediate points.

Players can explore any turn in a completed game, including their opponent’s trays and potential moves.

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