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.

The post Perplexity says News Corp tried to bait its chatbot into copyright infringement appeared first on The Media Copilot.

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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