Substack readers can now run a check on any post longer than 100 words to see how much of it an AI model likely wrote. The company rolled out the feature on July 21 in partnership with the detection firm Pangram, with the scan available on web and iOS and Android support promised later.

The tool works only on content published starting on July 21. Alongside it, Substack is giving writers a dedicated space to state whether and how they used AI in a given piece. The idea, per the company, is to set reader expectations rather than police anyone.

No AI detector is reliable enough to call a piece machine-written with certainty. Pangram uses a deep learning classifier trained on around a million documents rather than the older perplexity-and-burstiness heuristics, and it reports strong internal accuracy numbers. But The Atlantic examined how well Pangram actually performs and found the picture more complicated than the marketing suggests. False positives are a known problem across the category, and lightly edited or paraphrased AI text is far harder to catch than raw model output.

Substack did acknowledge those limits in its announcement post. It also hinted at further features tied to AI content and reader preferences without saying what they are.

Its acknowlegdment matters because a wrong flag carries real cost. A human writer who gets tagged as an AI user has little recourse in the moment, and the accusation sticks. Non-native English writers have historically drawn more false positives from detection systems, which raises fairness questions for a platform with a global base of newsletter writers. Pairing the automated score with a voluntary disclosure field is Substack’s hedge against that, letting writers put their own account on the record next to the machine’s.

For newsrooms and independent publishers, the move is a preview of how disclosure norms may harden across platforms. If readers start expecting an AI provenance label on every post, editors will face pressure to document their own use of the technology and to decide where the line sits between an AI writing assistant and AI ghostwriting. Publishers who have built AI into their workflows, a shift The Media Copilot has tracked closely, will want a clear internal policy before a third-party score answers the question for them.

The real test isn’t whether the detector works. It’s whether readers trust a score that Substack itself acknowledges can be wrong. The company is betting users will treat AI detection as one signal among many, not a definitive verdict.

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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.

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Within hours of Nicolás Maduro’s arrest by U.S. forces, fake AI-generated images of Venezuela’s ousted president spread across social media faster than newsrooms could verify them.

The incident marks one of the first times synthetic imagery has depicted a major figure during a rapidly unfolding news event, according to a New York Times report by Stuart A. Thompson and Tiffany Hsu.

“This was the first time I’d personally seen so many A.I.-generated images of what was supposed to be a real moment in time,” Roberta Braga, executive director of the Digital Democracy Institute of the Americas, told the Times.

Some fake images made it into Latin American news outlets before being quietly replaced with an official photo shared by President Trump. NewsGuard, which monitors the reliability of online information, tracked five fabricated images and two misrepresented videos that collectively drew more than 14.1 million views on X in under two days.

The Times tested a dozen AI generators and found most tools, including Google’s Gemini, OpenAI’s ChatGPT, and X’s Grok, quickly created fake arrest images despite stated policies against misleading content.

Jeanfreddy Gutiérrez, who runs a fact-checking operation covering Venezuela, said the fakes spread through “almost every Facebook and WhatsApp contact” he has before official images were available.

“It just took a lot of work, because we always lose the battle to convince people of the truth,” Gutiérrez told the Times.

A pattern of failures

That battle is getting harder. The Maduro deepfakes emerged just days after Grok became the center of a global regulatory firestorm. Governments in the EU, UK, France, India, Malaysia, and Australia have all launched investigations after the chatbot began generating non-consensual sexualized images of women and minors at scale.

Bloomberg reported that Grok was generating thousands of “undressed” images per hour earlier this week. The official Grok account posted an apology on X, writing that it “deeply regret[s]” generating sexualized images of girls “estimated ages 12-16.”

If a major image generator can’t prevent the creation of child sexual abuse material, its safeguards against political deepfakes are likely just as porous.

Gutiérrez said many people refused to believe the official image of Maduro posted by Trump was real.

“It’s funny, but very common,” he told the Times. “Doubt the truth and believe the lie.”

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The Associated Press rolled out AP Verify on Monday, packaging AI-powered verification features alongside traditional authentication tools in a single web-based dashboard.

The platform combines reverse image search, frame-by-frame video analysis, and social listening with AI-driven geolocation, object detection, transcription, and a chatbot assistant. It also includes generative AI text detection and team collaboration features.

“In an era of rampant misinformation and digitally altered content, verification is more essential than ever,” said Gianluca D’Aniello, AP’s senior vice president and chief technology officer.

“AP Verify equips journalists with the essential tools they need to assess online content quickly and accurately all in one place – whether it’s identifying the source of a photo, analyzing video or vetting text.”

AP has used the tool internally for a year before offering it to other publishers. The newsroom used it to secure original Texas flood footage by tracing it to its source, verify a viral meteor sighting in South Carolina, and find eyewitness video from the Charlie Kirk assassination, according to Aimee Rinehart, senior product manager for AI strategy at AP.

Before AP Verify, journalists relied on a “patchwork of tools” including Google reverse image search, Rinehart told Press Gazette. The platform integrates third-party providers including Google’s Fact Check, Trint for transcription, Graylark’s geolocation, GPTZero’s AI text detection, Trendolizer, and identity solutions provider Pipl.

“None of the tools are 100 percent,” Rinehart said. “We would never recommend you go straight to publish just based on that tool’s information.”

The platform surfaced a need among under-resourced newsrooms. During market research, a local broadcaster told AP they sometimes run the wrong tornado video and apologize the next day.

Each newsroom subscription keeps content private. Competitors cannot see what others are verifying.

The launch will test whether publishers want centralized verification tools, Rinehart said. She pointed to last year’s Kate Middleton photo manipulation incident, when five agencies including AP pulled a palace-issued image, as evidence that “AP became a source of truth” for publishers.

“That’s what we really want to get to,” Rinehart said, “is everybody trying to discern what is real, what has been retouched, and what can we trust online?

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