Media consumption recently passed a big milestone: people now turn to social media and video networks like YouTube for news more than any other source. The Reuters Institute’s Digital News Report, now in its 15th year, found 54% of audiences now rely on social and video platforms to get their news, putting them ahead of publisher websites at 51% and TV at 52%.

And the trust side of the ledger keeps getting worse. Just 37% of people say they trust most news most of the time, the lowest reading since Reuters started tracking it in 2015. In the United States the figure sinks to 25%. Gallup’s October 2025 poll landed in the same place, with U.S. trust in mass media at 28%, down from 31% the year before and 40% five years ago.

The natural read is that media brands matter less every year, drifting toward irrelevance as audiences scatter into feeds. AI chatbots seem to accelerate the slide. The Reuters report puts news consumption via AI chatbots at 10%, up from 7% a year ago. If brand erosion plus AI summarization is the trajectory, the long-term picture suggests publishers will eventually be reduced to information wholesalers, supplying the raw facts and quotes that someone else interprets, packages, and presents back to the reader.

That story has been the dominant framing for about two years now. But the data underneath doesn’t actually back it.

What the click pattern tells you

Most news consumption on social platforms is incidental. Posts and clips arrive between the workout tips and the gadget ads, and the Reuters report identifies a growing slice—now 12% of people and double the 2020 figure—who run into news only while they’re online for something else. That’s not really audience; it’s adjacency.

Behavior inside AI products reads very differently. Among people who click out of an AI answer, 44% do it to verify the news is correct, against 36% on search and 33% on social. Another 43% click to find out more about the source, versus 35% and 34%. Only 51% click for more detail, well below 59% on search and 60% on social.

That’s a behavioral signal worth paying attention to. Inside an interface designed to strip out bylines and erase visual brand cues, audiences are reaching back through the answer to get to the publisher who supplied it. The dominant reason isn’t curiosity. It’s verification. Readers don’t fully trust the summary, so they reach for the name they recognize to check it.

That breaks the simple “trust in news is collapsing” story. The aggregate trend is real, we don’t live in the aggregate. People can hold low trust in “the media” while continuing to rely on the specific publications they’ve read for years. The Reuters data confirms it: Overall trust fell in 29 of the 48 markets surveyed, but trust in the most widely used individual brands held its ground, with several major names sitting above the broader decline. Behavior and stated preference point at the same answer. Audiences are funneling toward names they already know.

The brand still matters. Arguably more than at any point in the last decade, because the brand is the only fixed object as the surrounding interface keeps changing.

Trust converts but not on impact

We should be realistic about the size of the audience that gets news via AI—it’s still only 10%, and just 1% call AI their main news source. But the slice is growing faster than any other channel, and it skews toward the most engaged readers. Among the biggest news lovers, 18% already use AI for news. That is the cohort every publication has been trying to win for the last decade.

The catch is that trust is not directly convertible. A reader who treats your name as a stamp of credibility inside a chatbot summary may never click. A reader who does click to verify a fact on your site likely arrives, scans, and bounces. Brand reliance at the moment of consumption often produces no measurable lift.

The conversion, however, can happen somewhere else. The reader who keeps reaching for your name to check the machine is the reader who eventually subscribes, who shares your work to a contact, who recommends the publication when a friend asks where they get their information. Reuters found that 46% of paying news consumers now cite values-based reasons for paying, rather than the specific content they’re buying. Those reasons accrue. The brand-reliance behavior happening inside AI interfaces is the leading indicator of the durable reader relationship that eventually shows up in revenue.

The practitioner work for the next 18 months is operational. To make the most of AI audiences, publishers need to build instrumentation that captures the moments when readers reach for the brand, even when the click numbers look thin. Build persuasion strategy that converts those signals into something countable.

Stop playing defense

The headline finding of the Reuters report implies a strategy for media: get on more surfaces, get on them harder, push more short-form video, and lean into the platforms that audiences actually use. Most publishers are following that script. On AI, the script has been the opposite, with many media sites blocking crawlers completely.

All of that is defense. While defense is important, if it’s your entire strategy, you will lose. The offensive posture is to fight to be the default name in your lane, the publication readers reach for when they doubt whatever the surface is showing them.

That means using social, but treating it as funnel rather than destination. Casual readers get a taste; the strategy is to convert a fraction of them into a brand relationship that survives outside the platform. It means blocking crawlers that take without permission, but pairing the block with clean, machine-readable paths for partners and licensees. It means producing the clip, but anchoring the clip to deep, comprehensive coverage that earns the reader’s return visit and, eventually, their subscription.

The creator economy points the same direction. About 27% of people now get news from creators who explicitly focus on news, and 46% from creators of any kind. Those creators score better than legacy outlets on relatability and entertainment value. They also rate lower on trust and impartiality. And the audience that watches them consumes more traditional media than the average reader, not less. Only 3% rely on creators alone. Creators introduce audiences to topics. The brands pick up the verification.

The fragmentation story is real. Audiences are scattering across more surfaces, taking news in smaller pieces, and getting more of it from formats that didn’t exist a decade ago. But the behavior underneath that fragmentation runs the other way. The more the news gets sliced up, the harder readers lean on a name they trust to tell them what’s actually true. Audiences take their news in smaller bites now, but the chef still matters.

A version of this column appears in Fast Company.

The post The news brand is the only thing AI users still click for appeared first on The Media Copilot.

By The Copilot & Musso Media Editor

Generative AI can now create high-quality images and videos in seconds. But as the technology accelerates, a more fundamental question is emerging:

Can AI-generated media ever be trusted at scale?

In this episode of The Media Copilot, Pete Pachal speaks with Dr. Yair Adato, founder and CEO of Bria, about a growing divide in the AI ecosystem. On one side are models trained on vast, scraped datasets. On the other are systems built around licensed data, attribution, and control.

“This is by far the most advanced technology humanity has created,” says Dr.  Adato. “…and it took six years, not fifty.”

At stake is not just quality, but ownership, accountability, and the future economics of creativity.

Listen or watch:

• YouTube
• Spotify
• Apple Podcasts

Why this matters

The generative AI boom has largely focused on what these models can do. Less attention has been paid to how they are built and who benefits.

As brands, media companies, and enterprises begin to integrate AI into real workflows, concerns around copyright, likeness rights, deepfakes, and data ownership are no longer theoretical. They are operational risks.

This conversation reframes the debate:

The future of AI may depend less on better models and more on building systems that businesses can actually trust.

What we cover

• Why “brand-safe” AI is becoming a business requirement, not a feature
• The case for licensed data and a new attribution-driven data economy
• How generative AI could reshape ownership and compensation for creators
• Why visual AI presents higher stakes than text models
• The limits of current models and the push toward greater control and transparency
• How enterprises are integrating AI into real production workflows
• The tension between automation and creativity in media and storytelling
• Why AI will handle the “average” and humans will still define what is exceptional

About the 👤 guest  

LinkedIn

Americans for Ben-Gurion University feature

Bria (official site)

Bria AI LinkedIn

Bria AI Instagram

Bria AI Facebook

About the show: To explore more conversations like this and see what’s new, visit the freshly updated Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube?  Tap the Like button and Subscribe to the YouTube channel.

For more AI tools and resources built for media professionals, visit MediaCopilot.ai.

Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team 

Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0

All rights reserved. © AnyWho Media 2026

The post The real battle in AI isn’t capability. It’s trust. appeared first on The Media Copilot.

Most newsrooms that have adopted AI policies have done something admirable — and insufficient. A new briefing from the Center for News, Technology & Innovation synthesizes 30 peer-reviewed research papers on AI governance and finds that existing policies get the principles right but skip the operational details journalists actually need to follow them.

The CNTI report, released Feb. 17, is the third briefing from the organization’s AI and Journalism Research Working Group. Reviewing policies from 52 global news organizations across 12 countries, researchers found that newsrooms consistently prioritize transparency about AI use, human supervision of AI tools and human verification of outputs. But few policies define what “appropriate” or “proper” AI use actually means in practice.

The gap matters. As one example, AI translation tools can introduce gender biases — assuming doctors are men, nurses are women — that a journalist using a third-party tool may never catch. Existing policies focus on AI outputs, not the systems that produce them, making these subtle errors nearly invisible.

The procurement blind spot is arguably the bigger problem. Researchers found almost no AI policies that address how newsrooms should evaluate, contract with or monitor third-party AI vendors. A 2025 study of 16 AI tool contracts found that most gave developers the right to change terms of service without notice — a risk most individual journalists aren’t even aware of. Meanwhile, newsrooms’ growing reliance on tools built by Google, Microsoft and Amazon deepens their dependence on the same platform companies that already control much of their distribution.

The policy gap isn’t limited to the Global North. A Thomson Reuters Foundation survey of 221 journalists in the Global South found that roughly 80 percent said their newsrooms have no AI policy at all. That number has likely improved since the survey was conducted, but the structural barriers — no access to technical expertise, difficulty getting organizational buy-in, the pace of technological change — haven’t gone away.

The working group’s practical recommendation: treat AI policy development the way you treat coverage decisions. Include people with different job responsibilities and lived experiences. Draw on the lessons of earlier technology policy cycles — photo editing, social media — where the same tension between values and operational specifics played out. And start thinking seriously about procurement: what your AI contracts actually say, who can change them, and whether your organization has the leverage to push back.

For most newsrooms, the answer to that last question is no — but knowing that is the first step toward addressing it.

The post New Research: Newsroom AI policies strong on principles, weak on practice appeared first on The Media Copilot.

AI is already embedded in how people discover and consume news, from search to chat interfaces to automated summaries. So the question is no longer whether journalism will be shaped by AI. It’s how newsrooms maintain trust while experimenting responsibly.

In this episode of The Media Copilot podcast, Pete Pachal sits down with Alex Mahadevan, Director of MediaWise and a faculty member at Poynter, to unpack what media literacy looks like now that anyone can generate convincing content at scale. Alex shares how his background in data and local journalism shaped his approach to tools, why public-facing AI ethics policies matter, and what it will take for news organizations to bring audiences along for the next phase of the information ecosystem.

Why this matters

Trust is the core product. AI can either widen the trust gap with errors and low-quality content, or help rebuild credibility through transparency, better products, and clearer communication about how journalism is made. This conversation gets practical about what responsible AI use looks like, where disclosures help and where they can unintentionally slow innovation, and why the newsroom AI divide is becoming a real competitive advantage for organizations that adapt.

What we cover

• Alex’s journey into journalism and the global mission of MediaWise

• How AI is reshaping misinformation, trust, and newsroom transparency

• Practical uses of chatbots, coding agents, and AI workflows

• The widening divide between AI enthusiasts and skeptics in newsrooms

• Ethics, job concerns, and gray areas around AI-assisted writing

• What the future of news may look like beyond traditional articles

• Watch on YouTube
• Listen or watch on Spotify
• Listen on other podcast services

About the 👤 guest 

🔗Alex Mahadevan

🔗Poynter / MediaWise 

🔗MediaWise

About the show: To explore more conversations like this and see what’s new, visit the freshly updated Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube?  Tap the Like button and Subscribe to the YouTube channel.

For more AI tools and resources built for media professionals, visit MediaCopilot.ai.

Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0All rights reserved. © AnyWho Media 2026

The post Fake news at machine speed: inside AI’s impact on media trust appeared first on The Media Copilot.