It’s official: Nobody likes AI slop. Trust in the answers AI assistants give for news has sunk to just 20% worldwide, against 37% for news overall, according to the Reuters Institute’s 2026 Digital News Report, out last month. That number lines up with everything else showing up in the research this year: Influencer marketing agency Billion Dollar Boy reports that preference for AI-generated creator content has dropped 44% since 2023, with sentiment now split down the middle between people who see AI as a positive force and those who see it as a negative one. A Fractl survey this spring found the share of consumers who consider AI helpful fell from 82% to 54% in a single year. In news specifically, audiences consistently say that AI labels make them trust a story less.

Once the cost of content went to zero, the backlash was inevitable. Content-analytics firm Graphite reported last fall that the rate of production of AI-generated articles had pulled even with human-written ones, briefly edging ahead in late 2024. Human writing still dominates the places people actually look, though. Graphite found 86% of articles ranking in Google, and 82% of those cited by ChatGPT and Perplexity, were written by people.

The lazy solution is to just ban AI content, and a few outlets have done exactly that. Medium barred it from its paid Partner Program, and the sci-fi magazine Clarkesworld had to pause submissions after a flood of AI-generated spam. But bans are a sledgehammer, and they take down more than the target. People who use AI, paired with human judgment, to enhance and improve their content get swept out with the spam.

Bans also assume the filters can tell the difference, which they mostly can’t. AI detection is notoriously unreliable and prone to false positives. Layer in sophisticated prompting plus the generational jumps in AI models, arriving every few months, and the whole thing turns into an arms race the detectors keep losing. What works today may not work tomorrow.

The better approach is the scalpel, not the hammer: cut away the poor, valueless AI content, but leave intact the AI-enhanced work that audiences appreciate. The way to do that is to point the filters at outputs and outcomes, not the mere presence of AI.

Two moves against slop

In the last couple of weeks, two large platforms made moves that show the split between these approaches. YouTube introduced new controls on certain content types, including the all-too-common AI-narrated video padded out with stock B-roll or generative imagery. Those videos, along with a few other cases, are now harder to monetize, which removes the main incentive to make them.

In the world of text, Substack rolled out an AI detector powered by Pangram. It shows up two ways. First, writers get a button on the publish flow that scans the post and returns an estimate of how much was written by a human and how much by a machine. Most writers already know whether they used AI, but for larger publications with guest contributors, the feature could work as an extra vetting step. Second, readers can scan any article on the platform for AI writing as long as it was published after July 21, 2026.

Again, to be clear: no one likes slop, and it should be disincentivized. But whether a piece is synthetic tells you nothing about whether it’s any good. It’s ultimately up to each reader what to do with that score, but Substack’s scanner quietly pushes a simple equation into the room: AI equals bad.

Then there’s the false-positive problem that has dogged AI detection since day one. A Stanford study on GPT detectors found they misclassified 61% of essays by non-native English writers as AI-generated, and at least one detector flagged 97% of them, while essays by native writers drew just a 5% false-positive rate. The tools were mistaking unfamiliar rhythm for machine output.

At platform scale, even small error rates get ugly fast. As one analysis noted, even a 1% false-positive rate would wrongly flag thousands of pieces a year at a single mid-size operation. Extrapolate that across a Substack, or a Forbes.

Target content, not process

The more durable approach is to look downstream of the detection step, which is essentially what YouTube did. YouTube has an easier problem, to be sure, since at its scale distinct abuse patterns show up fast. But the principle travels: bad content gives itself away in the behavior it produces. Repetitive formulas, weak engagement, high bounce rates, negative comments, and the rest.

For Substack, the fix is to name the behavior specifically. The detector treats “made with AI” as the thing worth flagging, when the real target is content that’s valueless to readers. Those aren’t the same thing. Substack would do better to name the behavior it wants gone and target it directly: posts that use a lot of words to say nothing, auto-generated digests with no human judgment behind them, or even whole publications spun up to feed crawlers instead of readers. If a brand stands up a Substack and pipes in bots to flood the zone with narrative-shaped filler, downrank it or clear it out. Detection can help there, as a signal on the back end. But the label a reader sees should be about the quality of the work, not the tools behind it.

In the interest of full disclosure: The Chatbox, the news digest inside my Substack newsletter, is built with heavy AI assistance. It’s also prompted against a knowledge base tuned to what media people need, edited by a human, and published because a person decided it was worth your time. The AI is already disclosed to readers. A provenance scan adds nothing to that and introduces a signal readers may use as a blunt filter.

Filters are easy, calibrating is hard

Zoom out and all of this is progress. Both YouTube and Substack’s moves point to platforms getting smarter about filtering. That’s good news for anyone in the business of making or reading things online. The market never cleaned up slop on its own, so having filters in place is welcome.

The open question is calibration. Get it right, and the internet a couple of years from now looks better than it does today: creators using AI to sharpen what they make, audiences getting more of what they actually value, and the slop filtered out before it clogs the feed and degrades everything. Get it wrong, and we’ve just built a more expensive way to distrust each other.

The filters are here, and mostly that’s welcome. The harder part is pointing them at the right targets.

A version of this column appears in Fast Company.

The post What YouTube and Substack got right and wrong about AI slop appeared first on The Media Copilot.

If you want to chart the shortest path to where AI in journalism gets uncomfortable, look at what happens the moment the machine is asked to write the story rather than just research it. It inevitably comes up, since the most obvious use case for generative AI is writing. It’s right there in the name—large language models (LLMs) are all about reading, organizing, analyzing, and conjuring words. That single fact is the reason so many working reporters have spent the past few years quietly recalibrating what their job even is.

The friction is no longer theoretical. As artificial intelligence systems get better at writing, a growing number of newsrooms are using AI to help not just with analysis, process, and ideas, but the actual words, too. That has surfaced real fights on the shop floor. Reporters at The Sacramento Bee recently objected to having their bylines put on content written primarily by AI.

I’ve been making this point in my AI trainings for a while now: putting AI-generated words in front of a public audience is one of the highest-risk applications of the technology. I should know, since AI-generated articles are a component of The Media Copilot’s editorial strategy. A playbook starts with an honest self-interview about what you’re doing: What is the medium? What exactly is the AI’s role? What’s the worst that could happen? There are more questions after those, and the answers turn into the rulebook everyone in the newsroom has to live by.

Arguably the most important section of that rulebook is disclosure, the mechanics of telling a reader when the words came from a machine. The most direct way to do so is with an AI byline. These usually get sterile, corporate-sounding names, such as the AI News Desk or Generative AI Services, along with their own author pages. That unambiguously lets the reader know that AI didn’t help just with research or ideas, but also with the words on the page. How much of the writing was actually machine-generated, which is usually the part readers care about, tends to live in a disclaimer at the bottom of the page.

Transparency has a price tag

On paper, the AI byline looks like a clean fix. It checks the transparency box, it’s an easy-to-read label, and it slots neatly into an existing system. Audiences even ask for it. A Trusting News study found that 94% of readers want disclosures on content that’s AI-written. All of which suggests the AI byline should be trending up as AI use in newsrooms climbs.

The trend line is going the other way. A 2025 audit of 186,000 articles in 1,500 newspapers in the U.S. estimated that about 9% of the content was partly or fully AI-generated, yet only about 5% of those articles included a disclosure. Prominent AI-byline experiments at Fortune and Business Insider were discontinued. That is not evidence of a broader retreat from AI writing. Nick Lichtenberg, business editor at Fortune, famously used AI to produce more than 600 stories in six months. At The Cleveland Plain Dealer, writing tools convert raw reporting into stories with final sign-off from the reporter, and the AI byline shows up only when the human contribution is minimal.

From my own perch covering this beat, the shift is visible: fewer robot bylines every quarter. Which isn’t to say they’re gone: CoinDesk marks AI assistance with the byline “AI Boost” and ESPN’s writing bots still get top billing, but they read as holdouts, not the direction of travel.

Three forces are pushing the AI byline out.

1. The visibility problem. Google says it doesn’t downgrade content simply because AI was used to produce it. Its guidance still leans hard on clear authorship, first-hand expertise, and accountability, though, and the company has said publicly that assigning AI an author byline is probably not the best way to disclose the use. A robot byline may not be a direct negative ranking signal, but it also strips out the human-authority cues that search and answer engines are built to reward.
   
   The data backs the intuition. In Graphite’s 2025 analysis, human-written articles made up 86% of the pages ranking in Google Search and tended to rank higher than AI-generated material. That doesn’t prove that AI authorship or attribution caused the difference, but the disadvantage is real and pointed in a consistent direction. That pattern shows up in our own experience at The Media Copilot. Our human-bylined articles show up in Google Discover and rank higher in Google Search than those published under our AI byline, The Copilot.
2. The trust paradox. The Trusting News study found that, although the vast majority of audiences want AI disclosures, their presence made 42% of respondents less likely to trust an article. Reuters Institute research surfaced the same shape at a wider aperture: 12% of readers are comfortable with fully AI news, versus 62% comfortable with fully human-written news. The label the reader asks for is the same label that erodes their trust when they see it.
3. Institutional memory. When generative AI was new, there were several high-profile failures of AI content. An AI byline welds a publisher to that history. By putting forward an AI byline, an outlet paints a target on every article that runs under it, ready for the screenshot industrial complex to fire at the moment something breaks.

Add it up and the AI byline has collected a lot of baggage in a short time, and plenty of publishers have decided it’s not worth carrying. However, that doesn’t translate into a pullback on AI generally, or even a pullback on AI-assisted content. The workaround most publications are quietly settling on is simple: keep the byline for the human and disclose the machine’s contribution in a note somewhere else on the page.

What the byline really is

The whole debate comes back to what a byline actually signals. The idea that the person named at the top of the article wrote each and every word has always been a fallacy. Editors, wire copy, fact checkers, headline writers, and spellcheckers all contribute actual words and sometimes whole passages to articles. Automated editing software takes this even further; anything substantially edited through Grammarly or a similar tool already carries wording shaped by AI.

The byline is not “I wrote all these words”; it’s “I stand by all these words.” And that unearths the biggest problem with AI bylines—they obfuscate responsibility. Without ownership, without someone prominently standing by what’s actually written, there’s little incentive to make sure it’s great writing. Every article that carries a robot byline gets marked as second-class writing, no matter how much of the work the AI actually did.

The Sacramento Bee episode cuts the other way, too. Writers will defend their names, hard, and they should. The path forward isn’t to force writers’ names onto AI content without their consent, but to give them the freedom to use the tools and take on the accountability that comes with signing their names to the output. The disclosure should scale with the machine’s contribution: Routine editing may need no note at all. Substantial drafting warrants disclosure. Largely automated reporting should spell out both the system and the human review that stands over it. Ultimately, though, the only label that really matters is whether a named human stands behind the result.

With the right policies and training in place, a publication can hand its writers wide latitude on AI while keeping accountability attached to a human. If the content is worthwhile, then over time those who use AI to amplify and accelerate their judgment will be successful. The ones using AI as a substitute for thinking will inevitably fail.

Human accountability is the scarce signal

So the AI byline isn’t dying, but it is being repriced. As machine text gets cheap, a human name that carries real accountability becomes the scarce and valuable signal. As the tools get better, they’ll continue to blur who wrote what. The constant, however, is simple: A human has to stand behind it. No one gets to outsource accountability to a machine.

A version of this column appears in Fast Company.

The post Why newsrooms are quietly retiring the AI byline appeared first on The Media Copilot.

Most media companies are now pretty aggressive when it comes to blocking AI bots from their content. Reports say most major publications almost universally block crawlers from major AI companies like OpenAI and Anthropic, and many small to midsize publishers mirror that, configuring their robots exclusion protocol settings, or robots.txt, to keep them out.

It’s a defensible choice. Sometimes it’s the right one. But it also crudely compresses a complex issue into a binary: Block or don’t block. And the reasoning is often just as straightforward, pointing to the fact that most AI platforms don’t give back significant human traffic. Meanwhile, the crawl volume has become punishing enough that infrastructure cost is a factor. From an ROI standpoint, it’s an easy call.

There are exceptions. Google is the big one, because presence in AI Overviews are is currently welded into Search itself, so slamming the door on Googlebot isn’t really an option for most media companies (although the balance of that equation is starting to shift). And if you’ve cut a licensing deal with a specific platform, its crawler obviously gets a pass.

The bigger problem with a pure block-or-allow framing is that it treats direct traffic and direct revenue as the only things worth measuring. That’s too narrow a window to really get a full picture of the opportunity AI presents. An AI-forward strategy has to be more thorough: sort the bots by function, understand how each one connects your work to an audience, treat that audience as real even when it’s not a human clicking through, and shape how your content actually appears once it lands inside someone else’s answer.

Sort the bots before you swing the axe

Different bots are doing different jobs, and lumping them together is where most publisher policies go wrong. There are several types, but broadly three matter most to publishers: training bots, search bots, and retrieval bots. At least those are the ones that operate inside the “legitimate” ecosystem—declared, documented, and mostly respectful of robots.txt. Set the unauthorized scrapers aside for a minute.

The one worth a closer look is the retrieval bot. Retrieval bots typically have “-user” as part of their name, and their job is narrow: fetch a specific page in the moment a person asks an AI chatbot a question. Their activity is modest and predictable, usually hitting only a small number of pages, and they can often be rate-limited or served cached content. Cloudflare, in fact, has made it easier to distinguish between uses like search indexing, real-time AI input, and AI training, and its AI Crawl Control tools now include options to allow, charge, or block specific AI crawlers.

Letting retrieval bots through, and possibly rate-limiting a small population of search bots, can materially change how often your work shows up in AI summaries.

The reflexive publisher answer is “so what?”—you can’t cash a citation at the bank. Which is true, but it misses the strategic point: If you’re not in the answer, someone else is. Every citation your competitor gets is a small deposit into their authority on that topic, and audiences follow the citations that keep showing up. The scarce referral traffic that does exist tends to route to whoever’s already trusted by the model. Winning in AI means understanding that authority is the prize, not traffic.

Signal value, don’t donate it

Chasing authority doesn’t mean surrendering everything to get it. Your most valuable assets—whether they be content, community, or experiences—need to remain yours, not given freely to AI systems. Strategic content is where the discipline shows: careful choices about what an AI can read versus what a reader still has to come to you to see. Metadata, snippets, access controls, and explicit instructions for LLMs are the levers. Used well, they can teach a crawler that something valuable exists on your site without handing over the substance.

Take an agriculture trade publisher with a heavy, data-rich report on how pesticides affect soybean demand. A snippet might describe exactly what kind of data is within and why the data is relevant to certain types of research while not revealing the data or conclusions. The report page itself can spell out what’s open, what’s restricted, and how a qualified user gets to the full document.

The end state is asymmetric on purpose: the AI knows the report is authoritative, but anyone who wants to actually read it has to come to the publisher and clear some kind of gate—a subscription, an email, a partner login. The goal isn’t to hide entirely from AI answer engines. It’s to make them aware of the publisher’s value without letting them reproduce that value.

Your archive is your alpha

There’s an interesting framing here. Palantir cofounder Alex Karp said in a widely shared CNBC interview, where he advised enterprise AI customers to stop using models from the major AI labs, since it was effectively giving them their “alpha”—the company data that gives them an edge over competitors. Media companies, unfortunately, didn’t get to make that choice cleanly. Most of what publishers produce is public, and much of it was already ingested into the first wave of foundation models.

Those same lab-built models are now competitors, not just infrastructure. Answer engines keep users on the platform instead of routing them to the outlet that produced the reporting. This is, of course, the foundational idea behind the many lawsuits and licensing deals between the AI companies and the media.

But the market has moved past the original training-data fight. To give users the best and most current answer, the model needs live information at the moment of the query, and that shifts the value from training rights to retrieval rights. Rob Kelly has a useful analysis of this shift, showing that training rights are no longer a given in publicly announced AI licensing deals. In his database, only about 4 in 10 public 2026 deals include training rights, a sign that the market is moving from “buy content to build better models” toward “license content to deliver better answers.”

Most publishers aren’t going to land their own deal with OpenAI, Anthropic, or the rest. That’s the honest baseline. It doesn’t mean there’s no way to extract value from your content in an AI marketplace—it means the first step is making your content machine-readable in the first place, which is a separate discipline from bot blocking.

The number of places publishers can market their content to AI experiences is growing. Factiva, for example, includes content from thousands of suppliers, all licensed and Microsoft describes its new Publisher Content Marketplace as a way to support licensed access to premium content while preserving publisher control, independence, and sustainable revenue. The most ambitious move is to build your own agentic layer that legitimate customers and partners can hit via MCP (model context protocol).

That requires real engineering, often with a partner, but the payoff is control over the experience. Rather than letting some third-party crawler scrape and interpret your material, you’ve already done the interpretation, and any authorized external bot gets the version you’ve pre-processed — a relay, not a raw pull. That’s the whole idea behind AI media projects like Reuters’ new MCP server, which allows customers to search, retrieve, and use the Reuters content they subscribe to inside AI workflows.

I’ve made the case before that publisher-built agents and AI-ready archives change the shape of the business: once you’ve done the hard work of formatting, ingesting, and processing your archive for AI, you start to look less like a content supplier and more like a tool vendor. Reuters clearly recognizes that publishers who don’t build some kind of controlled retrieval layer over their archives are letting third-party crawlers set the rules. Eventually, that will lead to negotiating from weakness.

Now a word about the bad actors. Unauthorized scrapers hoover up huge parts of what publishers produce and then sell that data in various black and gray markets. Certainly, this is where blocking should be encouraged, and publishers need both reliable tools for doing so as well as broader ecosystem support, such as what Cloudflare has done to better identify bots and potentially monetize their activity. As I wrote earlier this year, unauthorized AI crawling is rampant, and publishers need more than wishful thinking and a robots.txt file to deal with it.

Turn defense into leverage

Blocking, on its own, is a defensive posture, and defense doesn’t win the AI cycle. The answer is not to throw the doors open or nail them shut. It is to build a gate with rules. Give retrieval bots enough to make your authority visible. Keep training crawlers and unauthorized scrapers away from the material they have no business ingesting. Use snippets, metadata, paywalls, and rate limits to separate discovery from access. Then do the harder work behind the gate: clean the archive, structure it, make it legible to machines, and expose it through channels you actually control.

The giant AI licensing deal is not coming for most publishers. That’s fine—it was never a real business plan. The better play is to make your work legible to AI systems without making it free, so when the market does come looking for trusted answers, you’re not begging to be included. You’re already the gate.

A version of the column appears in Fast Company.

The post The bots publishers should be letting through the door appeared first on The Media Copilot.

Speed has always been oxygen in the news business, and the 2010s gave newsrooms an extra reason to breathe deeply. When search and social were the main pipes to readers, the pressure to publish first was constant. Especially around major live events like the Oscars or the Super Bowl, the pressure to post fast often meant preparing “shell” stories in advance, with potential headlines and background information already included.

I’ve made this point before: AI has a tough time with breaking news. Because it takes time for facts to be verified and a consensus to emerge about what happened, AI systems—and in particular Google—tend to shy away from summarizing events in the early minutes or hours of a news event. You would think, then, that speed is a diminishing asset in an AI-mediated news environment.

The reality is messier. Some news publishers are pushing in the opposite direction, opting to publish faster, and with more stories, in the wake of breaking news. For its World Cup coverage, USA Today prepared several shell articles around major games, as Digiday reported. Internal AI systems helped accelerate that process, with human editors altering and publishing them as the games developed. USA Today had already tested the approach during the Winter Olympics and got enough of a lift to run the same playbook, at greater scale, at the World Cup.

Getting into the citation pool early

Fast-turn news isn’t the innovation here. The AI layer is. It’s unclear how long it takes for Google to create an AI Overview around a breaking topic. The Digiday piece cites one test in which AI Mode had access to a breaking story’s information within 10 minutes. AI Overviews appear to move more slowly: One SEO consultant said he had seen them appear within about four hours, and sometimes as long as half a day, while acknowledging there isn’t a lot of good data to go on.

Google may need hours to formulate an AI Overview, but USA Today’s results suggest early publication still pays. Being part of the initial set of sources that compose the answer bestows an advantage for ongoing inclusion—as long as the engine treats you as authoritative and the piece maps to the queries readers are actually typing into AI search.

This is why treating shell articles as an ongoing strategy, rather than a one-off, matters. Having multiple stories around the same topic, linking to each other, is a strong signal. It doesn’t hurt that USA Today is a major domain. There’s also a reporting factor at work: USA Today reporters are physically at the games, gathering exclusive quotes, facts, and perspectives in the follow-up. AI sees all of that and notes the pattern as it considers what to include in a summary.

So is there a first-mover advantage? The evidence is mixed. Being early to a story likely factors into inclusion. Muck Rack analyzed more than one million links cited by major AI systems and found that the highest citation rate occurred during the first seven days after publication. Recency shapes what gets picked, but the first article to hit publish doesn’t automatically beat the fifth.

The takeaway for AI: early counts more than first. And speed is only one input. Established authority—either on a topic or in the news media broadly—is clearly an advantage. A study from SEO tools company SE Ranking that analyzed 75,550 AI Overviews found that, among recognized news outlets, 10 publications received almost 80% of all mentions. The BBC, The New York Times, and CNN alone accounted for 31%.

The unit of competition has changed

The deeper shift is that the ranked link is no longer the unit newsrooms are competing over. Search rankings still matter, but they are increasingly feeding something else: a cluster of sources that an AI system uses to compose an answer. In that world, ranking is a means. Being one of the sources the answer can’t leave out is the actual goal.

The prize isn’t only the click anymore. It’s presence, citation, and narrative authority, the chance to help set the terms of the story before the reader ever lands on a publisher’s site.

That reshapes the newsroom playbook without discarding it. The job is to prepare for predictable uncertainty: map the outcomes you can foresee, the questions readers are likely to ask, and the context an AI system will need to grasp why the event matters. Before news events, consult with your team and AI on possible outcomes, the stories you’d create, and the search queries that people are most likely to ask. Choose the stories you want to be authoritative on, and use AI to help prepare shells and ensure that all your staff is trained up to know what to do.

The trap to avoid is publishing an empty container with a headline and a promise of updates. The winning article is fast, but not thin. It answers the obvious question, supplies the necessary context, links to relevant background, and shows evidence that someone is actually reporting the story. That means writing for two audiences in a single draft: the human who wants the latest developments, and the machine deciding which sources belong in the answer. Background, links, metadata, original quotes, clear sourcing, and visible updates all become part of the same authority signal.

Reporting is still the moat

Then push that authority beyond the first article, not by spraying the same story everywhere but by reinforcing the reporting where readers and AI systems already go to confirm it. The follow-up analysis can become a short video, a podcast segment, a newsletter item, or a social post, and the goal is consistency, not duplication. AI is a great accelerant, but not a replacement for reporters or reporting.

The metrics also have to catch up. Clicks still matter, but they will undercount the value of this work. Newsrooms need to know whether they’re present in AI answers, whether their reporting is showing up (and how prominently), and whether their original facts and framing are making it into the summary. Traffic share is only half the picture. Share of the answer is the other half.

The tactics are there for publishers with the actual reporting to back them up. Speed still creates the opening. Authority determines who owns the answer—and whether winning it is worth anything.

A version of this column appears in Fast Company.

The post Why authority is the new speed appeared first on The Media Copilot.

The New York Times and a group of other publishers are asking a federal court to sanction OpenAI, accusing the company of withholding or destroying evidence in a high-stakes copyright case over how ChatGPT was trained and used.

In a motion filed Thursday in federal court in Manhattan, the publishers alleged that OpenAI misrepresented its ability to search training datasets and ChatGPT output logs for copyrighted news material. According to Reuters, the publishers said OpenAI told the court it could not search its large language models for their work while allegedly concealing that it had already done so “even before the first News Plaintiff filed suit.”

The motion is the latest escalation in the copyright fight between major news organizations and AI companies. It also moves the dispute deeper into discovery, where the question is not just whether AI companies can use journalism to train models, but whether they can preserve, search and produce the records needed to prove what happened.

The plaintiffs include The Times, the New York Daily News and other media organizations, including Ziff Davis and the Center for Investigative Reporting, according to The Associated Press and Variety. The original New York Times article reported that the publishers are seeking legal sanctions against OpenAI, including monetary penalties and other remedies.

The filing does not ask for sanctions against Microsoft, which is also a defendant in The Times’ broader copyright case, according to The Times’ summary of the motion. Microsoft has invested heavily in OpenAI and integrated OpenAI technology into products including Copilot.

“The evidence is in OpenAI’s training data sets and ChatGPT output logs,” the publishers said in the motion, according to The Times. “But instead of just producing that evidence at the start of the case and focusing on the merits of its fair use defense, OpenAI chose obstruction.”

OpenAI rejected the allegations. “As the Times’ case weakens and they’ve been forced to drop claims against us, they’re persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations,” OpenAI spokesperson Drew Pusateri told Reuters. “We’ll continue defending our users’ privacy and the long-established principles of fair use.”

The publishers allege that OpenAI deleted billions of relevant ChatGPT conversations or made them unsearchable. They also argue that an OpenAI employee later testified that the company had performed multiple searches for news publishers’ content, contradicting earlier representations about the company’s technical limitations.

A sanctions memorandum posted by Ars Technica says the publishers want the court to bar OpenAI from relying on a disputed 20 million-log ChatGPT sample, find that ChatGPT’s output logs include or would have shown substantial use of the publishers’ copyrighted material, instruct the jury on those findings and award fees and costs tied to the discovery fight.

Those remedies would matter because discovery disputes can shape the trial record. If the court finds OpenAI failed to preserve or produce relevant evidence, the ruling could affect what arguments OpenAI can make later and what conclusions a jury may be allowed to draw from missing or incomplete records.

The Times sued OpenAI and Microsoft in 2023, alleging that millions of Times articles were used without permission to train AI systems that now compete with publishers as sources of information. OpenAI and other AI companies have argued that training models on large bodies of text is protected by fair use, a theory now being tested across lawsuits from authors, artists, music labels and news organizations.

For publishers, the issue goes beyond training data. They argue that AI chatbots and AI search summaries can answer readers’ questions using journalism without sending traffic, licensing revenue or subscribers back to the organizations that reported the information. Media Copilot has been tracking the same pressure point in coverage of Google’s AI accuracy problem and The Times’ warnings about AI companies using journalism without permission.

At the same time, publishers are taking different approaches to the AI economy. Some are suing. Others have signed licensing deals with AI companies. The Associated Press announced a deal with OpenAI in 2023, while other media companies have made agreements with OpenAI, Google, Meta and Amazon.

The sanctions motion could increase pressure on both sides. A ruling against OpenAI would give publishers leverage in court and in licensing talks. A ruling for OpenAI would strengthen the company’s argument that publishers are using discovery to intrude into user privacy and commercially sensitive systems.

Either way, the case shows that AI copyright fights are becoming data-governance fights. The central questions are no longer only what AI systems were trained on. They are whether companies can prove it, search it, preserve it and explain it in court.

The post Publishers ask court to sanction OpenAI in escalating copyright fight appeared first on The Media Copilot.

It’s hardly a revelation to say that Google’s AI Overviews sometimes get things wrong. The Gemini-written summaries at the top of search results have been misfiring on and off since they debuted in mid 2024. It feels like Google will never fully live down the infamous “glue on pizza” moment, and the errors come often enough that they always carry the warning, “AI can make mistakes, so double-check responses.”

Nonetheless, AI Overviews are now the reality for anyone (read: everyone) who uses Google. At some point, publishers have to stop treating each new mistake as a curiosity and start treating the system that produced it as their working environment.

This spring, The New York Times commissioned AI startup Oumi to measure the problem. The ultimate finding: The latest version of AI Overviews was accurate 91% of the time. That looks respectable until you run the math against Google’s billions of daily queries. A single-digit error rate at that scale produces millions of bad summaries every hour.

The Times drove the point home by citing BBC tech reporter Thomas Germain, who ran an experiment. He published a fake blog post crowning himself the world’s best hot dog eating tech journalist. Within a day, AI Overviews were repeating the claim, apparently without checking.

The stunt looks silly because the query was silly. But the underlying mechanism isn’t. Germain succeeded largely because he owned the only page anyone had ever written on that subject. It was an information vacuum. For a well-covered topic, a lone rogue post would barely register.

The lens publishers can’t remove

The hot dog stunt is only one failure mode; it turns out AI answer engines can go wrong in several ways. And the stakes for publishers keep rising: AI Overviews now appear in most searches. An April report from AI-visibility startup QuickSEO put their prevalence at 60.23%, and that was before Google’s May I/O conference tightened the loop between AI Overviews and AI Mode, letting users slide from a summary into a conversational follow up without leaving the results page.

Chatbots aren’t the biggest surface here. Google is. People can opt in to ChatGPT or Claude, but they get served AI Overviews whether they want them or not. That default status is what makes accuracy such a load-bearing question. Publishers can’t set the terms of the lens their work passes through, but they still have skin in the game once it does.

Ubiquity isn’t the same as blind acceptance. Trust in AI answers scales with the stakes of the question. A roast chicken recipe gets less scrutiny than a cancer treatment query, even if the entry point is identical in both cases.

By the time a reader decides to double check an answer, the framing has already landed. The summary supplies the vocabulary, sets up the follow up questions and points to what feels worth investigating next. If a publisher the reader trusts is cited in the summary, confidence rises even when the citation is never clicked. I’ve made the case before that citation is a form of value for publishers, but that value depends on the reporting being accurately represented.

Three ways the machine gets it wrong

To map how AI Overviews fail, I spoke to Isis Blachez, the AI lead at Newsguard who runs the organization’s AI False Claims Monitor. She sorts the failures into three buckets, and each one shows up in the Times study.

1. Weak or irrelevant material rises to the top. This is the glue-on-pizza scenario. That recommendation came from a Reddit post written as a joke (we hope), which made it irrelevant to a serious cooking query. The catch is that the post did answer the question head on, and direct answers rank well in AI discoverability. Journalistic content generally performs better in AI engines when it’s optimized for machines. When it isn’t, or when it’s blocked outright, thinner material can grab an outsize share of the response.
   
   “We do [reliability] ratings of news sites,” explains Blachez. “And we saw that for most of the highly ranked sites, they were blocking a lot of the AI bots, and then most of the low-quality sources were giving full access to AI web crawlers.”
2. The AI finds the right source and misreads it. This is the quietest failure mode and possibly the most consequential. Blachez points to a case where multiple chatbots cited Snopes to confirm a false claim that Iran had attacked a Pakistani flagged oil tanker. The Snopes piece was actually the debunking. The machine flipped it.
   
   “Sometimes, even if it’s citing a credible source, it can be incapable of citing it well or retrieving the information correctly,” Blachez says.
   
   The reporting itself is fine in these cases. The machine is the point of failure. This version of the problem is the one that often features in lawsuits against AI companies.
3. The information pool has been poisoned on purpose. The hot dog story is the innocent version of this. The pro-Kremlin Pravda network is the malicious one. It flooded the web with millions of articles across sites designed to look like news outlets, pushing Russian narratives at industrial scale. Coordinated actors publishing similar sounding claims across many domains can manufacture the appearance of consensus and crowd out honest reporting in retrieval systems.
   
   “So what we’ve observed that worked with Pravda is flooding search results,” says Blachez. “It’s like putting the same information with practically the same language, many domains, many times and just dominating narrative on that specific topic.”

Building the machine readability pass

So the answer layer can go sideways because access is blocked, the material is manipulated, or the content itself invites misreads. The AI operator has an obvious duty to raise the floor on quality. What about the publisher?

A lot of newsroom people have quietly written this problem off as somebody else’s, on the grounds that AI systems are a black box. That framing is understandable and mostly wrong. Publishers can influence all three failure modes. Being in the mix means not being blocked. Discouraging misreads means writing for machine comprehension as well as human. Beating manipulation means publishing your own answers to the queries you want to own.

Blocking crawlers is a legitimate choice. Copyright and the absence of any compensation model are real reasons to shut the door. And when journalism is blocked, Google and every other AI company still owe their users a duty of care with the material they do use. But when journalism is available to the AI, publishers have levers to make sure it’s represented correctly.

Every newsroom already runs an SEO pass on its work. The most effective way to shape what AI Overviews and chatbots surface is to run a machine readability pass alongside it. This isn’t just standard GEO hygiene like matching titles to common queries. It means writing so that the tricky parts of a story remain unambiguous to a machine reader, even when they’re already obvious to a human.

In practice, that means saying the quiet part out loud. A human understands that “alleged” applies to a whole run of paragraphs even when the word only appears once. A machine may not carry the qualifier forward.

A short set of questions to run through the pass:

• Are dates explicitly tied to the correct events?
• Is it clear whether an allegation is being reported, verified or debunked?
• Is the primary conclusion stated plainly rather than left entirely to implication?
• Are corrections and updates obvious?
• Does the article distinguish the original source from later repetition?
• Does the headline create ambiguity that the body later resolves?

As with SEO, editing for machine clarity tends to sharpen the human read too. The trade off is that the pass improves the odds. It does not guarantee anything. The goal isn’t “AI proof” journalism. The goal is to strip out avoidable ambiguity and give accurate reporting a better shot at surviving the answer layer.

Publishers can’t dictate what Google says about their work, and they shouldn’t be expected to patch the flaws in someone else’s product. But as AI settles in as a default filter between journalism and its audience, treating that as a reason to disengage stops being a strategy. Newsrooms can still make the truth easier to find, harder to misread and much harder to replace.

A version of this column appears in Fast Company.

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A year ago, Cloudflare drew a line in the sand against unbridled AI crawling of the internet. Exactly one year later (again on Canada Day) the company took what it says is the next major step on that journey, introducing new tools for publishers and content creators to not just block bots from crawling their content, but charge them for access.

To me, the most interesting part of this is the new Pay Per Use framework. This builds on the existing Pay Per Crawl system, which charged bots whenever they crawled a page. But that straightforward approach didn’t necessarily capture the value of the crawl—once captured by an AI crawler, a piece of content could be used multiple times, in hundreds or even thousands of answers. On the other hand, something could be crawled and never used at all.

Pay Per Use fixes this by compensating the content owner when their content is actually used in an AI answer. Theoretically, if you published something unique, valuable, and optimized for machines to read, it could end up paying dividends for as long as people ask about it. And knowing that is part of the new system, too—Cloudflare promises analytics for content owners so they know how their content is being used. It’s also going to have a better system of telling bots when content hasn’t been updated so they don’t keep re-crawling the same static page over and over.

The system sounds like a sensible evolution to Pay Per Crawl—at least for inference (i.e. AI search engines). For AI training bots, Pay Per Crawl actually strikes me as the better solution since it’s more “one and done.” And how would you measure the value of an individual piece of content in a training set anyway?

All of this depends on a workable payment system, of course, and Cloudflare shared details on how it’s evolving that part of the framework. The new Monetization Gateway is straightforward: 

• a bot tries to access content
• the gateway responds with the payment needed and how to pay
• the bot deposits the payment and gets a proof of payment
• The bot then re-requests the content with the proof
• the gateway checks it and bestows access.

It’s all nice in theory, but this kind of usage-based pricing becomes a bookkeeping nightmare on the content owner’s side. This is one of the big reasons micropayments never took off in digital publishing—the revenue from a small payment by a single customer was never worth the processing hassle.

Cloudflare says its unique position as a content delivery network helps solve these problems. It’s already tracking and classifying the bots, so it’s easy to add the payment credential to the process. There’s no “account creation” or anything like that—the bot just shows the receipt. And it’s all done on an open protocol, with no checkout pages or separate payment API. Apparently, there are advantages to managing traffic for 20% of the web.

Cloudflare is refreshingly honest that its new Pay Per Use system is an experiment. How this all plays out depends largely on adoption, not just by publishers but also by AI companies and data brokers. Lots of people often say that digital publishing needs its Napster moment—when the music industry transitioned from sketchy Napster downloads to the “legit” option of iTunes. But iTunes downloads were aimed at individuals. Nobody typing a search into Google or Claude is deciding what content to pay for. This is all determined at the company level, and companies will always choose to get the best/most data for the least cost.

And that will ultimately come down to a simple equation: Is it less costly to get the data they want via Cloudflare’s system? If it’s not, it will remain merely an experiment.

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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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The AI industry pumps out so much hype that you’d be forgiven for simply shrugging at the recent release and sudden withdrawal of Anthropic’s Fable 5 model. Set against the Elon Musk vs. Sam Altman trial and Anthropic locking antlers with the Department of War, the Fable episode could read like just another week in AI.

This one is worth paying attention to. This is really the first time the government has stepped in to regulate a specific model release on the grounds that its capabilities could pose a national security risk in the wrong hands. Whatever happens next, the line has been drawn: a frontier model in general release can be taken off the board because Washington decides it’s too dangerous to leave widely available.

For anyone building AI into their daily work, that shifts the calculation in a real way. The intelligence available to you isn’t only a function of price anymore. It’s also a function of policy, geography, the terms you’re willing to accept on your data, and whether the vendor or the government leaves the model running at all.

The story behind the freeze

For readers who don’t track model releases closely, here’s the short version. Fable 5 is the first generally available model in what the company is calling its “Mythos-class” models, a tier above Opus that Anthropic says has crossed a meaningful risk threshold in cybersecurity and biology. Fable 5 is the consumer-safe version, built on the same underlying Mythos 5 model but wrapped in extra guardrails designed to block or downgrade certain cyber, biology, chemistry, and model-development queries. It also jumps Anthropic’s core model number, signaling a generational step forward from Opus 4.8, Sonnet 4.6, and Haiku 4.5.

Then, on June 12, three days after launch, the government ordered Anthropic to block Fable 5 and Mythos 5 from every foreign national, including foreign-national employees working inside the United States. Anthropic said it could not reliably enforce that distinction and disabled both models globally. The trigger, by most accounts, was a suspected jailbreak that punched through Fable’s cybersecurity guardrails. Anthropic disputed the severity of the finding, saying the demonstration uncovered only minor, previously known vulnerabilities that other public models could also identify.

That fight is still going on. Cybersecurity leaders have urged the government to reverse the order, arguing that defenders need access to the same capabilities and that comparable tools are already available from American and Chinese competitors. Anthropic is working to get Fable back online, and rival labs will almost certainly ship something comparable in short order (some are already claiming to have done so).

The specific dispute may resolve in days or weeks. The precedent will outlast it. A model can be released, integrated into workflows, and then disappear because a government draws a line around who may use it. For anyone building around a single model or vendor (and “building” might simply be leveraging it in crucial, strategic use cases), availability is now part of the risk calculation.

What people saw before the lights went out

Early users got just enough time with Fable 5 to confirm Anthropic’s claims about it. Despite controversies over how Anthropic chose to limit how Fable 5 deals with queries the company deems risky (more on that in a minute), users are seeing the power of the model. Fable 5 is designed for agentic work, meaning it can work autonomously on tasks for a long time, sometimes hours or days, without losing context. The advice that came out of those early sessions was consistent: stop using frontier AI like a fancy autocomplete. The best way to use it, many say, is not to ask it to perform straightforward in-and-out tasks like writing an essay or telling you the best parts of a lengthy report, but to give it broader goals about what you’re trying to achieve, let it build the plan, then execute, however long it takes.

That window was short, but it counted. It showed this level of intelligence is no longer a slide in a research deck. The model was pulled back, but the capability threshold remains crossed.

A big part of what makes Fable work is that it grades its own homework. If you’re a regular user of Anthropic’s models, you’ll notice there’s no “Thinking” mode for Fable 5. That’s because adaptive thinking is always on: The model decides when and how much to reason on every request, and at higher effort levels it can reflect on and validate its own work. Tasks turn into loops. As it works to achieve the goal, it can try things, evaluate the results, change course as needed, and try again. And it can do so autonomously.

For media and marketing teams, the practical shift is in scope. Instead of, say, assigning it to design a specific email campaign, or help format your newsletter, you can zoom out and tell Fable 5 to conceive and build an entire marketing strategy around your newsletter. That might involve reformatting your templates, building new landing pages, adjusting the publishing schedule, building a social campaign, and more. Theoretically, with the right access, it could then build all of that for you. Your job is to grade the output. Over time, less of that grading happens mid-process and more of it happens at the end.

That’s the promise anyway. The danger is that organizations may begin designing around that promise before access, cost, and governance are stable enough to support it.

Fable 5 is the first model that puts real agency on the table. Right now, working with agents, while powerful, involves a lot of management: ensuring the plan the agent builds is correct, clearing up barriers that it encounters as it performs the task, and then guiding it to the best output, usually through multiple iterations on the task itself. In theory, a model strong enough to evaluate its own intermediate work shouldn’t need that hand-holding.

That gap between theory and practice is the real story of the freeze. For a few days, users could test a different relationship with AI; then the capability vanished. We crossed the threshold in the lab and lost it in the market on the same week.

The three walls between you and frontier intelligence

Fable 5 and the models that will follow it stand to change how we work with AI, and arguably how we work, full stop. However, using Fable 5 to its full potential was never just a matter of selecting it in your model picker or calling the API and letting it cook. The pullback put a sharper point on a problem that was already there: the most capable models are also the hardest to actually deploy. I see three walls in the way, with a fourth that just got built.

1. Access and context. For an organization to use Fable 5 to its full potential, it would require a large amount of access to the right context (the org’s information and data). Here, Fable’s strength tripped over itself. Because Anthropic fears the model could be misused, it requires prompts and outputs from Mythos-class models to be retained for at least 30 days for safety monitoring, including in enterprise environments that would otherwise use zero data retention. Anthropic says the data will not be used to train models and that, on some third-party platforms, it remains inside the customer’s cloud environment. But companies cannot use Fable 5 under a true zero-retention arrangement.
   
   That retention requirement, plus the restricted categories where Fable 5 quietly throttles down to Opus 4.8, has set off real friction with enterprise buyers. Many companies will be reluctant to cede control over how their own data is retained and reviewed. Microsoft reportedly limited employee access while its legal teams assessed the implications for confidential and customer data.
   
   And on top of all that sits the new wall. Even if a company accepts the privacy terms, secures the integrations, and builds the right internal controls, the model can still disappear because of a government order or vendor decision. Serious agentic systems will need fallback models, portability across vendors, and a plan for what happens when the most capable model is suddenly unavailable.
2. Compute. Fable 5 is not cheap. Anthropic priced it at $10 per million input tokens and $50 per million output tokens, twice the price of Opus 4.8. I’ve written before about how the agent era is squeezing compute budgets at every layer, and with AI hardening into a political wedge issue, expect compute pressure to stay tight for months and probably years.
   
   The premium price doesn’t automatically kill the math. Some early users argued that it could solve hard tasks in fewer turns than weaker models, potentially lowering the total cost of completing the work. Still, that argument only holds if the work was worth doing with a frontier model in the first place.
   
   If Fable 5 and its peers are going to act as the brains at the top of a company’s AI stack, the deployment question is going to need actual rigor. Organizations will need to be very selective of how to deploy it: which tasks to assign to it, who should have access, and what guidelines, rules, and restrictions there need to be on usage.
   
   And there’s an awkward irony in talking about allocation right now. Intelligence can be technically achievable and commercially valuable while still being unavailable.
3. Task imagination. I became aware of the term “task imagination” through the AI Daily Brief podcast, which references a video by the AI strategist Nate B. Jones. In his take on the Fable 5 release, Jones makes the simple observation that not many knowledge workers think about their work in terms of tasks that might take days to do. It requires a certain level of strategic thinking that may not actually apply to many roles. Put bluntly: a model can run for two days, but most workers have never been asked to define a goal worth two days of machine effort.
   
   For media practitioners, that’s the part worth sitting with. An editor might call on the model to develop more granular editorial guidelines and style guides based on different article types (news, features, evergreen explainers, etc.). Reporters might build investigative agents that don’t just surface data in document troves, but develop research plans based on leads and then execute on them by mining remote databases, filing FOIA requests, and other complex touchpoints that typically require human involvement.
   
   The catch is that most jobs aren’t scoped that way. Many jobs have narrow definitions of what the work is, and there’s little motivation to go beyond that. A model that can do days of work isn’t very useful if the work it’s given is still measured in minutes. That puts pressure on workers to imagine more ambitious tasks or risk being left behind.

The paradox of a pause

The Fable 5 pause comes wrapped in a paradox. A pause gives organizations time to build the governance, data practices, and strategic habits needed to use this level of intelligence responsibly. The trouble is, task imagination only develops with hands on the model. Without access, people cannot discover which long-running assignments are worth the money, where agents fail, or how their own roles could expand around them. The pause buys time while taking away the main way to use that time well.

Step back, and a clearer picture forms. A future where we’re working alongside agents will encounter serious barriers beyond just capability (and political freak-outs over that capability). We restrict access to context so neither the tool nor its creators knows too much. We limit how much we spend on models because we’re unsure of the return we’ll get. And many of us throttle our ambition with AI since our jobs simply don’t have a rich enough canvas for a model like Fable 5 to fill in.

A fourth restriction now sits on top of those three: the model itself may simply not be available.

For media leaders trying to make the ROI case, that’s a problem. The strongest demonstrations depend on giving capable models real work, real context, and enough time to execute. When the most capable models are pricey, hemmed in, or suddenly absent, teams drift to safer pilots that are easier to approve and unlikely to move the underlying economics.

None of these walls fall just because someone ships a smarter model. While advancements in security, infrastructure, and work redefinition will help us get past them, those are inherently slower than the rapid advancement of AI.

We pushed past one threshold and walked straight into several walls. I suspect the story of Fable 5 will be looked back on not primarily as a step up in power, but as the moment where the implications of that power pushed the limits of the systems meant to use it. Agentic AI is clearly where this is going. The systems around it need a beat to catch up.

The pause is useful, but it isn’t free. Experimentation is how organizations learn what this intelligence is actually for. For now, AI leaders are about to discover that running frontier AI at full strength is harder than proving the strength exists. The pure experimentation phase is over. The reality check phase has started, and access, cost, control, and utility now matter every bit as much as raw intelligence does.

A version of this column appears in Fast Company.

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The European Commission has published a voluntary Code of Practice on Transparency of AI-Generated Content, giving AI providers and deployers a concrete path to compliance with the AI Act’s labeling requirements—and a clear reason to sign up.

The code, released June 10, 2026, covers two broad categories of obligations. Section 1 targets providers of generative AI systems, requiring them to mark outputs—audio, image, video, and text—in machine-readable formats and ensure their detection as artificially generated or manipulated. The technical solutions must be effective, interoperable, and reliable “as far as technically feasible,” factoring in content type, implementation costs, and the state of the art. Section 2 targets deployers, requiring them to label deepfakes (audio, image, or video that falsely appears authentic) and disclose AI-generated or manipulated text publications on matters of public interest.

The Commission also released a set of standard icons that deployers can use to label AI-generated content. Nicholas Diakopoulos, a professor at Northwestern University, shared them on LinkedIn:

The code is currently under adequacy assessment by the Commission and the AI Board. Once it clears that review, signatories can rely on its measures to demonstrate compliance with Article 50 of the AI Act, reducing administrative burden and gaining legal predictability across all EU member states. Non-signatories will have to demonstrate adequate compliance individually, assessed case-by-case by national market surveillance authorities.

Signatories also gain access to Signatory Taskforces: working groups set up to share implementation practices and advance marking and detection techniques across the value chain.

The code is described as a “consistent, practical and proportionate” implementation framework, not a replacement for the AI Act or the Commission’s forthcoming guidelines on Article 50’s scope.

The code was developed over three drafting rounds between September 2025 and June 2026, led by an independent chair and vice-chair. Participants included AI system providers, detection developers, industry associations, civil society organizations, academic experts, and organizations with expertise in transparency and very large online platforms. International and European observers also contributed without voting rights. Two dedicated working groups handled the providers and deployers tracks separately.

Key milestones included a first drafting round starting November 5, 2025, a second round in January 2026, a third round in March 2026, and a closing plenary on June 10, 2026—the same day the code was published.

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