The New York Post is putting its founder’s name on its latest push into artificial intelligence. Starting Tuesday, readers using the New York Post and California Post apps will have access to Hamilton, a suite of personalization tools built around an AI chatbot, according to Axios.

The name is a nod to Alexander Hamilton, who founded the paper in 1801. The tools are designed to help readers discover more stories and receive recommendations based on what they read, according to Ariscielle Novicio, chief technology officer and senior vice president of product and digital strategy at New York Post Media Group. The company plans to bring Hamilton to its websites later.

Hamilton includes four features: the Hamilton Search chatbot, the Post Express personalized briefing, the Picked For You recommendation engine and Post Voices, a tool for discovering commentary. Each draws from current and archived reporting produced by the two newsrooms. The system does not generate original stories or make editorial decisions.

The system runs on Google Cloud, using its Gemini Enterprise Agent Platform and Gemini models. The Post sets the editorial rules and controls the product, while Google provides the infrastructure, AI models, search and recommendation technology, Novicio said. New articles are indexed and made searchable within seconds of publication, allowing the apps to offer recommendations based on the latest coverage.

Google Cloud already works with publishers on backend infrastructure, but this marks the first time it has powered an AI chatbot for a news partner in North America, according to Michael Clark, president of Google Cloud North America. The distinction is significant. Google’s Gemini Enterprise platform is moving deeper into newsrooms, providing technology that publishers can build directly into their products rather than simply sending readers their way.

Novicio drew a clear line around the relationship. It is a standard enterprise cloud deal, she said, not a Google News partnership or a content-licensing agreement. The Post pays Google for the technology, and the arrangement is separate from Google Search. The Post’s archive, which powers Hamilton, is also not being used to train Google’s general-purpose AI models.

That distinction reflects how News Corp., the Post’s parent company, has approached AI. It has signed licensing deals with OpenAI and Meta while suing Perplexity over what it says is the unauthorized use of its journalism. Paying for cloud technology to build its own AI products offers a third approach.

The launch comes as publishers look for new ways to hold onto readers while AI-driven changes cut into search and social referral traffic. Some news organizations are responding by building chat and search tools designed to keep readers on platforms they control. The Washington Post launched an AI chatbot in 2024, Time introduced one with its Person of the Year coverage, and Gannett and The Independent have worked with Taboola on generative search tools built around their own archives.

For now, Hamilton has the advantage of living inside the Post’s apps. The harder test will come on the open web, where readers have plenty of other places to ask questions — and little reason to stay unless Hamilton gives them one.

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Speaking during the company’s fiscal fourth-quarter earnings call on Wednesday, News Corp CEO Robert Thomson said artificial intelligence is “only as useful, only as trustworthy, as the quality and integrity of its inputs.” Without professionally produced journalism and other creative work, he argued, AI systems risk leaving users with a “slimy sea” of low-quality output, as reported by TheWrap.

News Corp, which owns The Wall Street Journal, the New York Post and publisher HarperCollins, is pursuing two tracks at once. It has licensing deals with OpenAI and Meta and says it is in “advanced discussions with several other honorable companies.” At the same time, it is suing companies it accuses of using its content without permission, including a countersuit against Brave over alleged scraping of WSJ and New York Post articles.

Thomson also named names. He said Perplexity remains a target of News Corp’s legal action and took aim at Brave, calling it “a company brave in name only.” He accused the company of using masked web crawlers to collect copyrighted articles and repackage them for business customers.

“Companies who buy from these pirates should know that they are in possession of stolen goods,” Thomson said.

But his broader warning was about who could face legal action next. News Corp, he said, is looking beyond the AI companies scraping and using its content to the businesses buying their products.

“We’re focusing not just on companies that have scraped and stolen our content, but on their clients who, knowingly or unknowingly, have purchased stolen goods,” Thomson said. “As for litigation, it’s far from over.”

Thomson also pushed back against the idea that licensing deals are a bad trade for publishers as AI search cuts into referral traffic, saying that the agreements with Meta and OpenAI are not “merely transactional.”

“We are creators; they are savvy distributors,” Thomson said. “Our inputs are crucial components of their outputs.”

Thomson confirmed that the Meta deal, reportedly worth $50 million a year, is already contributing to News Corp’s business. He said future agreements could include broad deals with major platforms as well as narrower licensing arrangements tailored to specific industries.

For other publishers, News Corp’s approach suggests that licensing and litigation are not really separate strategies. The company is using lawsuits to pressure AI companies to pay for access to its journalism, while licensing deals show what that access can be worth.

If News Corp expands its lawsuits to businesses that buy AI products, rather than limiting them to companies accused of scraping its content, more companies could face legal risk for using AI tools built on unlicensed journalism.

“There are more deals to come,” Thomson said in his final remark. “And hopefully not too much litigation.”

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Trust in AI answers rises and falls with the credibility of their sources. More than 90% of people trusted an AI response when they completely trusted its cited source, but that figure plunged to about 10% when they completely distrusted the source, according to new findings reported by Press Gazette.

The figures come from an Ipsos survey of 1,000 UK adults for the Association of Online Publishers’ Artificial Intelligence Publisher Impact Study. The findings show that AI can distill reporting into a polished answer, but users ultimately judge that answer by the credibility of the news organization it cites.

The effect extended even to publishers respondents felt neutral about. Trust in the AI answer was only about 25% when the cited source was rated neither trusted nor distrusted. The survey measures correlation, not causation, and reflects stated attitudes rather than observed behavior. Even so, the pattern is unmistakable.

Trust also affected reported willingness to visit the underlying publisher. Nearly half of people who somewhat or completely trusted a cited source said they would click at least one link in an AI response. That finding fits a wider pattern covered in The Media Copilot’s reporting on news brands and AI clicks: the publisher’s name may still be what persuades users to leave the chatbot, even as AI interfaces give them fewer reasons to click elsewhere.

There is an uncomfortable counterpoint. Even when users completely trusted the cited source, nearly a third said they would stop at the AI answer rather than click through. That figure was similar among those who completely distrusted the source. A citation alone can make an AI answer more credible without delivering the visit, subscription opportunity, or advertising impression that paid for the original reporting.

The study also found that 37% of respondents did not know AI tools can fabricate information or sources. That rose to about 45% among 45- to 54-year-olds. The result matters because a polished answer can borrow authority from reputable citations while still getting a claim wrong, omitting context or inventing a detail.

AI companies have good reason to seek current, attributable reporting. OpenAI’s partnership with News Corp is one public example of the licensing deals that give chatbots access to professional journalism. Yet the confidentiality of many licensing deals makes it difficult for publishers to know whether they are being fairly compensated for the credibility their reporting brings to AI answers.

For newsrooms, the immediate task is less about winning back every lost referral than preserving the conditions that make their reporting worth citing. Clear attribution, strong archive pages, distinctive analysis and direct audience relationships matter when an AI answer becomes the first read. Publishers also need to decide which crawlers and products earn access.

The survey’s implication is clear. AI platforms are borrowing trust that publishers spent decades building, while publishers risk capturing only a fraction of the value it creates. As AI answers become the default gateway to information, the rules governing licensing, attribution and publisher control will determine whether publishers can still turn trust into revenue.

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News Corp has fired back at Brave Software, filing a countersuit that accuses the search company of “flagrant theft” for distributing and selling copies of Wall Street Journal and New York Post articles to AI companies. The Tuesday filing in an Oakland, California, federal court escalates a legal fight Brave started more than a year ago, as reported by Reuters.

Brave sued News Corp preemptively in March 2025, asking a court to declare that bundling copyrighted articles for licensing and resale is not copyright infringement. It filed after receiving a cease-and-desist letter from the Rupert Murdoch-controlled publisher. Brave revised its complaint in May 2026, following what News Corp described as failed negotiations for a market-based licensing deal.

Now News Corp is on offense. In its filing, the company argued that Brave’s “covert scraping” and resale of copyrighted articles fall “nowhere near the bounds” of fair use.

“The more content Brave copies and sells, the more revenue it generates, and the less incentive AI companies have to negotiate licenses with the publishers who produced the content,” the lawsuit said. “Brave profits while publishers are cut out.”

News Corp wants an injunction, unspecified monetary damages and statutory damages of up to $150,000 per infringement. CEO Robert Thomson framed the case bluntly, saying Brave’s conduct showed “blatant disregard” for how information gets disseminated.

“This era of tacky tech trafficking must come to an end if journalism is to have a sustainable future,” he said.

Brave sees itself as the underdog. It has described itself as the smallest of three U.S. companies running independent search engines at scale, behind Google and Microsoft’s Bing. In its own filings, Brave argued that indexing News Corp content to make it searchable, and serving users snippets and “high-level summaries,” qualifies as fair use. It also accused News Corp of threatening to disrupt generative AI, which it called what many consider “the most important innovation so far this century.”

The dispute is one piece of growing litigation over copyrighted content and AI training. The New York Times’ ongoing case against OpenAI and Microsoft remains the highest-profile example, and courts have yet to settle what fair use means when publisher content feeds AI systems. The New York Post, Dow Jones and News Corp’s British and Australian operations are all defendants in Brave’s suit.

For publishers, the fight matters beyond News Corp’s balance sheet. Brave’s business model depends on packaging searchable web content into a data feed that AI developers can buy. If that survives a fair-use challenge, it undercuts the licensing deals publishers have been signing with AI firms. If it doesn’t, intermediaries selling scraped content face steep liability.

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The latest turn in the Dow Jones and New York Post case against Perplexity is less about one chatbot answer than about how copyright evidence gets built in the AI era. According to Press Gazette, Perplexity is asking the court to force the publishers to turn over records showing the prompts they used to test its system before filing suit.

Perplexity’s argument is blunt. In a filing quoted by Press Gazette, the company said, “This discovery would reveal an inconvenient truth: Plaintiffs repeatedly and deceptively crossed the line from investigation to entrapment.” In other words, Perplexity is not just denying infringement. It is arguing that the publishers tried to engineer failure conditions to make a stronger case.

That matters because publisher lawsuits against answer engines and generative AI tools often depend on showing that a system can reproduce or closely mimic protected reporting. If courts start scrutinizing how those tests were constructed, the evidentiary playbook for future cases could get more complicated.

Press Gazette reported that Dow Jones and the New York Post oppose producing the prompt records, arguing they are protected attorney work product created in anticipation of litigation. The dispute now sits at an awkward but important junction: publishers want to demonstrate copying while AI companies want room to argue that the tests did not reflect normal user behavior.

The prompt-log fight could matter beyond Perplexity

The legal issue here is narrow, but the industry implication is broad. AI companies have spent months insisting that many public examples of harmful output, hallucinations or copyright problems come from adversarial prompting. Publishers, for their part, have strong incentives to probe systems hard because casual use may not expose the outer edge of reproduction risk.

Press Gazette described one example in which Perplexity summarized a Wall Street Journal article but refused a request to reproduce part of it verbatim. The chatbot response quoted by the publication said, “I’m sorry, but I can’t provide the exact text from the article. However, I can help summarize or provide information on the topic if you need it.” That example supports Perplexity’s broader point that the system may resist some direct copying requests. But it does not settle whether other prompts produced output that went too far.

For publishers, that is the danger in this stage of the litigation cycle. Courts may begin asking not only what the chatbot returned, but how many tries it took, what sequence of prompts got there and whether those prompts resembled ordinary use. That is a harder factual record to present cleanly than a simple side-by-side reproduction claim.

Why newsroom leaders should care

This case is not just another skirmish in the running AI copyright war. It could influence how publishers and newsroom counsel document future complaints against search-answer and RAG-style products.

If Perplexity succeeds in forcing disclosure of prompt logs, plaintiffs may have to assume their testing methods will be examined in detail. That could make legal teams more rigorous about documenting why a prompt sequence was reasonable and how closely it matched actual user behavior. It could also give AI defendants a repeatable strategy: shift the discussion from output alone to the testing design behind the output.

There is still an important unknown here. Press Gazette’s report leaves open whether the judge will require disclosure of the prompt records. Until that happens, the case remains a procedural fight with larger implications rather than a clear substantive win for either side.

But the underlying issue is not going away. As publishers try to prove that AI systems copied their work, and AI companies argue that plaintiffs had to game the system to show it, courts will increasingly be asked to decide where legitimate investigation ends and manufactured evidence begins. That line could matter almost as much as the copying question itself.

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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.

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