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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For the last two years, I’ve published an annual set of predictions on how AI will alter the media business in the next year. It’s a tradition that feels increasingly like forecasting weather during a hurricane: bots keep multiplying, newsrooms keep contracting, and the next business model keeps hovering just out of reach.

Last year, four of the five predictions I made came true: the spread of audio experiences like NotebookLM’s audio overviews, a greater emphasis on content licensing, more “legit” AI-generated content, and publishers doing more with their own summarization and chatbots. The miss was agents. They were an unavoidable buzzword this time last year, but they ultimately ran into serious barriers keeping them out of the mainstream (data privacy and complexity being the main ones).

This time the task is even more challenging. Many trends, like AI adoption in newsrooms, are further along, which you would think makes their effects easier to predict. But the reality is that the most impactful things happen when those trends slam into realities, such as Cloudflare taking a hard stance against AI ingesting publisher content without compensation or consequence. Who saw that coming?

So, with the usual caveats, and a healthy respect for chaos, here’s how I think AI’s presence in media evolves over the next year:

1. The copyright issue gets worse before it gets better

The lawsuits keep piling up, and yet the core question still sits there unresolved. Publishers want compensation for how their work is ingested and repurposed; AI companies keep leaning on fair use. Licensing deals are spreading, sure—but they’re not resolving the underlying conflict so much as papering it over.

What’s different now is attitude. More publishers have decided the AI industry isn’t merely “scraping” but effectively freeloading, and they’re responding the only way they can: by blocking AI crawlers. That’s a rational move for publishers, and a brutal one for AI products, because it cuts them off from the freshest and best data—the very thing that makes them useful in the first place.

And then there’s the asymmetry. This doesn’t apply to Google, because it uses the same crawler for search and AI, and no publisher in their right mind would ever block Google Search. That gives Google a competitive advantage at a time where OpenAI just went into “code red” for fear of falling behind. Similarly, Perplexity is now the target for legal action from both News Corp. and The New York Times for how it summarizes their content.

If you’re an AI company trying to outrun Google, you’re stuck in a nasty bind: respect copyright rigorously and you fall behind; push too hard and you invite more blowback, legal or otherwise. Even OpenAI—tremendously successful by any reasonable standard—apparently sees the threat as existential. In that environment, the “do the right thing” incentives get weaker, not stronger. My expectation: Not only will AI companies avoid making moves that broadly support content providers (such as enabling them to block user agents), they may even become more brazen about ignoring safeguards like the Robots Exclusion Protocol.

2. AI focus in newsrooms shifts to product and revenue

A year ago, the story of AI inside newsrooms was just getting permission to use it. When The New York Times opened the doors for AI use by its staff, it was a signal that experimentation had moved from taboo to tool, especially for workflow tasks like transcription and social media management.

Now the center of gravity is shifting from efficiency to monetization. The launch of more sophisticated AI-infused products like Time’s AI Agent—which turns the publication’s vast archive into a grounded, AI-ready corpus—points to a future where publishers build AI products that can plausibly impact the bottom line.

Will those products create revenue? Still unclear. The path from “cool demo” to sustainable cashflow is longer and bumpier than deploying an AI headline writer, and the internal politics can be ugly (Politico recently got into hot water with its newsroom union over an AI tool for its lucrative Politico Pro division). But the upside is real enough, and the pressure intense enough, that more publishers will keep chasing it.

3. PR’s lean renaissance

For years, “go direct” PR was supposed to make the middleman unnecessary. If brands could publish their own stories, build their own audiences, and talk around the press, why keep paying for gatekeepers? That argument never fully killed PR, and now AI is reinvigorating the whole industry.

AI engines don’t just look for a single authoritative source; they sniff for credibility across domains and platforms. That makes broad citation, sometimes even on smaller, less glamorous sites, more valuable than it’s been in a long time. If your goal is to show up in an answer box, a widespread footprint suddenly matters again.

But AI also turns the screw on PR’s cost structure. Since much of PR work involves content, and it doesn’t have the same audience relationship that has kept almost all journalism authentically human, there’s mounting pressure from clients to use AI content generation to cut costs. The net result is a PR industry that’s strengthened, but also forced to be sharper, faster, and leaner than before.

4. Authenticity reasserts itself

Early generative AI panic centered on a simple nightmare scenario: journalism replaced by machine-written sludge. AI did move into newsrooms, but the wholesale displacement never really happened. That’s wasn’t because AI can’t research, analyze, and write, but because authorship is part of the product.

Readers don’t just consume information; they build a relationship with the people and institutions delivering it. Swap in AI authorship and that relationship changes, often for the worse. In other words, human authenticity is back in style, and, ironically, AI can help amplify it rather than erase it.

That’s especially true in formats where production costs used to be the barrier. AI can still be an accelerant here, helping more publications adopt video formats like the Times’ “explain the news” vertical shorts. If AI keeps pushing costs down, the decision to expand onto a new platform becomes less about budget and more about audience opportunity, as it should be.

5. Continued prioritization of owned audience

Even if we’re not headed for Google Zero, publishers shouldn’t get comfortable. A world of “Google Smaller” is still a world where SEO dependence is a liability, and where every algorithm tweak can feel like an emergency.

So the flight to owned audience continues. Publishers will keep shifting energy toward direct, habitual relationships: proprietary apps, newsletters, memberships, and live events—formats that tend to deliver higher engagement and better data. The catch is obvious: the more organizations that chase “direct,” the harder it becomes to stand out. Owned audience is the strategy; differentiation is the fight.

And the broader adoption curve keeps moving. It may still be early days for AI, but we’re well past the point of no return. More and more people are using it for information discovery (34%, up from 18% a year ago, per the Reuters Institute) and journalists continue to adopt AI as part of their workflows (more than half now use it at least once a week). The industry is clearly adapting to the new AI reality, and whether or not we get clearer answers to the big questions around copyright and business models, 2026 might be the year the media’s AI survival manual gets written.

AI was used to lightly alter this column from one that originally appeared in Fast Company. Media Copilot editors carefully edited the new version.

Frequently Asked Questions

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The New York Times filed a federal lawsuit against Perplexity on Friday, accusing the AI search startup of repeatedly violating its copyrights despite 18 months of demands to stop.

The suit, filed in New York, claims Perplexity’s search engine grabbed large chunks of Times content, including entire articles, to generate responses for users. The Times argues this directly competes with its own offerings.

“Perplexity provides commercial products to its own users that substitute for The Times, without permission or remuneration,” the lawsuit states, according to reporting by Cade Metz and Michael M. Grynbaum in The New York Times.

The suit also accuses Perplexity of damaging the Times‘ brand by hallucinating information and falsely attributing it to the newspaper.

Perplexity dismissed the lawsuit. “Publishers have been suing new tech companies for a hundred years, starting with radio, TV, the internet, social media and now A.I.,” Jesse Dwyer, Perplexity’s head of communication, told the Times. “Fortunately, it’s never worked, or we’d all be talking about this by telegraph.”

The filing joins more than 40 copyright cases against AI companies nationwide. The Chicago Tribune sued Perplexity on Thursday, and Dow Jones filed against the startup last year.

Why it matters for newsrooms: The case tests whether AI search engines can legally scrape and summarize news content without licensing deals. Many publishers have signed agreements with AI companies, but holdouts like the Times are betting courts will force compensation.

In September, Anthropic agreed to pay $1.5 billion to book authors and publishers after a judge ruled the company illegally downloaded copyrighted books. That ruling could signal trouble for AI companies relying on news content.

The Times struck its first AI licensing deal with Amazon in May. Financial terms were not disclosed.

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