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.
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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
AI is transforming media across multiple fronts: automating routine reporting tasks, enabling personalized content delivery at scale, helping newsrooms analyze audience data more effectively, powering AI-driven search that changes content discovery, and raising fundamental questions about revenue attribution and editorial responsibility.
AI is unlikely to replace journalists wholesale, but it is reshaping required skills. Routine data-driven stories—earnings summaries, weather alerts, sports scores—are increasingly automated, freeing journalists for investigation and contextual reporting that requires human judgment. Newsrooms that adapt early benefit most from AI augmentation.
AI is disrupting revenue on two fronts: AI-powered search is reducing referral traffic that supported ad models, while AI tools simultaneously help newsrooms cut production costs and improve subscription conversion. Publishers need new strategies that don’t depend entirely on search-driven traffic.
Key concerns include AI-generated misinformation, proper attribution when AI assists reporting, bias in models used for content decisions, data privacy when personalizing reader experiences, and transparency with audiences about how and when AI is used in news production.
Newsrooms should audit which workflows benefit from AI assistance, invest in staff AI literacy training, develop clear editorial policies on AI use, experiment with reader engagement tools, and monitor how AI-driven search is changing their traffic and subscription acquisition patterns.
