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.

By The Copilot & Michele Musso

For many journalists and publishers, artificial intelligence feels like the next existential threat…a technology capable of flooding the internet with cheap content, eroding trust, disrupting search, and making it even harder for real journalism to survive.

But what if AI could also be part of the solution?

On this episode of The Media Copilot, host Pete Pachal sits down with Paul Gewuerz, host of Small Press, Big Ideas and founder of LocalPod, to explore what is actually happening on the front lines of local media.

After more than 120 conversations with publishers, editors, entrepreneurs, and local news operators, Paul has seen firsthand how deeply challenged the industry remains. But he has also discovered something that rarely makes the headlines: new ideas are taking root.

From local newspapers transforming themselves into cafés and community gathering spaces to publishers building new revenue streams, launching podcasts, embracing events, and using AI to accomplish work that once required entire teams, local journalism is being reinvented in unexpected ways.

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

Pete and Paul discuss why trust may become even more valuable in an internet overwhelmed by AI-generated content, how small newsrooms are already using tools like ChatGPT and Otter.ai, and why AI could give independent publishers the ability to launch products and businesses that simply weren’t possible before.

They also confront the darker side of this transformation, including AI slop, fake local news sites, politically funded “pink slime” operations, and the growing challenge of knowing what information…and which sources…can actually be trusted.

In this episode:

• Why local journalism remains vital to healthy communities and democracy
• How innovative publishers are reinventing the local news business model
• Why trust could become journalism’s greatest advantage in the age of AI
• How small newsrooms are actually using AI today
• The opportunities AI creates for new products, revenue streams, and branded content
• Why AI-generated local news and “pink slime” sites pose a growing threat
• How podcasts can help local publishers grow audiences and deepen community relationships
• Why Paul believes AI represents a new industrial revolution
• The uncomfortable reality of building with AI: if you can create something faster, so can everyone else

Why this matters

For Paul, the promise of AI is personal. After spending more than two years building a software platform with limited progress, he used AI-assisted coding tools to complete it in just two months.

“I’ve been working on a software platform for my company for two and a half years, had about 10% done. I have finished it in the last two months. It is operational. People are on the platform.”

His experience raises one of the biggest questions facing media today:

What happens when suddenly anyone can build almost anything?

About the 👤 Guest

Paul Gewuerz on LinkedIn: Paul Gewuerz

LocalPod website: LocalPod.co

Small Press, Big Ideas on LinkedIn: Small Press, Big Ideas

About the show:

To explore more conversations like this and see what’s new, visit the 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

Episode Transcript

This transcript has been lightly edited for clarity and readability.

Introduction

Pete Pachal (00:34)

Hi, welcome to The Media Copilot. It’s a podcast about how AI is changing media, news, and communication. I’m your host, Pete Pachal. I covered tech for a long time as a journalist, and now I have deep conversations with the media people, the builders, and the creators who are all answering the question: How will we get information in the future? And how will that transform journalism and the business of media?

My guest today is Paul Gewuerz, host of Small Press, Big Ideas. That’s a podcast about local news in the United States and the people trying to make it work. Paul talks to publishers, editors, entrepreneurs, and local news operators about what’s working, what isn’t, and the future of community journalism.

I was recently a guest on Paul’s show, and we had a lively conversation about AI and local news and search and trust and all the things. So I wanted to flip the microphone this time and get his view from the front lines of local media.

Local news really is where a lot of the AI debate gets very real. These organizations are usually understaffed and underfunded, but they’re deeply tied to their communities. AI could potentially help them cover more ground, build more products, reach new audiences, and save time. But it could also flood the zone with cheap content and make trust even harder.

So we’re going to talk about what Paul’s hearing from small publishers and how local newsrooms are actually using AI. Where’s the risk? Where’s the opportunity? And what does community journalism look like if AI becomes part of the basic infrastructure of media?

Before we get into it, please take a second to rate or review the show. It really would help a lot. If you’re listening on Apple or Spotify, that might mean leaving a five-star review and maybe a nice comment. And if you’re watching on YouTube, please like the video and subscribe to the channel. Those things really do help people find the show.

All right. Housekeeping over. Paul, welcome to The Media Copilot.

Paul Gewuerz (02:46)

Pete, thanks, man. Thanks for having me on. Good to talk to you again. It’s been a few months, or a few lifetimes in the AI world and media world. So yeah, good to be here.

Pete Pachal (02:49)

Yeah, likewise, man. Totally. I think it’s like 500 Claude versions ago.

Before we get into AI and all the stuff around community journalism and local news that I just talked about, let’s talk a little bit about you. I’d love to hear more about your history, your background, and what brought you to covering local media in this way.

From Audiobooks to Local Journalism

Paul Gewuerz (03:20)

Yeah, I’d love to. I’ve said it a million times on my podcast: I’m not a journalist. I don’t come from a journalism background. I’ve always had an interest in it. In high school, I was really attracted to more gonzo journalism. I was a big fan of Hunter S. Thompson.

I went to school for journalism for a few years and graduated in 2008, so not a great time for the job market. I went into an entirely different field. I actually worked for a beer distributor for about a decade.

Pete Pachal (03:58)

Okay. I feel like that would have been great in 2008, with everyone wanting to drink their sorrows away.

Paul Gewuerz (04:18)

It was great. The beer industry does good in a good economy and better in a bad one. That’s kind of the internal line, anyway.

I worked there at a big corporation, a household name, for a long time and eventually got frustrated with the large corporate structures.

I’ve been told that I have a good voice, so I actually got into narrating audiobooks. I did that freelance for a few years, left my corporate gig, and eventually got out of that freelance, feast-or-famine mindset.

I’m a big audio guy, so I started producing podcasts for clients, social media influencers, content creators, etc.

A few years back, I was approached by a local news outlet in the Seattle area to produce a podcast for them, and it reignited that interest in journalism, specifically local journalism. We put together a podcast for them, and I got really interested in it. I wanted to work more in the space, started reaching out to more publishers, launched my own podcast, Small Press, Big Ideas, and I’ve just tumbled down a rabbit hole of media and specifically local journalism.

I’ve had a crash course in it over the last few years. I went into it initially as a business interest. I thought, “This is an interesting niche to target.” Then, after talking to people, I realized how vital it is to democracy and a community.

There are studies showing that when a local news source disappears in an area, creating what’s referred to as a news desert, corruption and financial misdealings at the city and county level skyrocket because there’s no accountability.

So besides the need for good-quality local news and information, it’s a vital thing for our society. I didn’t expect to tumble down that rabbit hole, but that’s where I’m at.

Today, I host the Small Press, Big Ideas podcast, and I have a company called LocalPod.co, where we specialize in producing podcasts for mostly all-digital publishers. But specifically, my heart is with local media operators and helping them grow audience and revenue from there.

That’s pretty much the story in a nutshell, I’d say.

The Untold Stories of Local Media

Pete Pachal (06:15)

I feel like with local media, there are obviously networks and groups that cover certain regions and that sort of thing. But generally, I don’t know if there’s a lot of communication outside of those things.

I feel like your podcast really provides a good service by creating conversation around that layer of media.

Everyone talks about local media almost at arm’s length, in the third person. “Wouldn’t it be nice if we had more?” But I feel like the actual newspapers are rarely part of that discussion. It’s usually just people opining on them, or whatever they are, not necessarily newspapers.

I think you’re providing a valuable service by giving folks an outlet. Also, people love to talk about their communities and themselves, and as you’ve found, I’m sure there are tons of unique stories out there in terms of success in journalism.

Paul Gewuerz (07:20)

Yeah, it’s a bigger topic than I realized. When I started the podcast, I thought maybe I could get 10 people I’d researched to come on. I’m 120 episodes deep now and still have people lined up. There are a lot of interesting stories out there.

I had Steven Waldman on the podcast early on from Rebuild Local News, an advocacy group out of Washington focused on strengthening local news. I think he’s the one who put it best in terms of local media sustainability.

He said it’s like there’s a forest fire. The last 20 years of Google and Meta and everything else have decimated the local media industry. But there are all these little green shoots and sprouts coming up. You wouldn’t know it from looking at the side of a mountain, but if you look closely, they’re there.

That’s what the podcast has shown me. There are a lot of cool stories and innovations happening. It’s just not necessarily at the scale we need yet.

Pete Pachal (08:16)

I’d love to hear about some of those. Are you thinking about anything specific when you think about the promising things being seeded right now?

Paul Gewuerz (08:22)

I’ve had a lot of people on the show, and every organization is different. Every community is different, and this is a huge country. The podcast is mostly based in the U.S., although we’ve had a couple of people from the U.K. and Canada.

I’ve had nonprofits on. I had somebody from South Carolina who left the legacy newspaper in town and started basically a glorified Substack. Three or four years later, they’re a nonprofit that works mostly on sponsorships, and I think they have a newsroom of four or five, maybe five or six, full-time people now. It’s become this vital thing to the community.

For a Canadian example, The Green Line up in Toronto is really interesting. It was founded by Anita Li, who was also on the podcast. I really like the design. They’ve built The Green Line to be very social media-native. Everything is visually appealing. Even the functionality of the website is different from what you think of when you imagine a newspaper site.

They create in-depth guides on things like housing and the job market, and they’re very practical. It’s not just an article you’d read. It’s a different format, and they’re crushing it.

Those two come to mind, but I could go on and on. There are a lot of examples.

The Hard Reality of Running Local News

Pete Pachal (10:04)

I’m glad you brought up Anita Li’s operation. I actually used to work with her at Mashable. She’s great.

We’ve talked about some specific examples, but let’s zoom out a bit. What’s your broader perspective on local media now that you’ve talked to more than 120 people and heard so many stories? What do you understand about local news now that you didn’t when you started the show?

Paul Gewuerz (10:36)

You hit the nail on the head when you said everybody holds it at arm’s length and says, “Yeah, we need more good local journalism.” And almost everybody who says that also says, “Well, I’m not going to pay for it.”

That’s a reality.

I think it was a mistake made by the news media industry early on in the internet era to put everything up for free. People got used to that, and it’s very hard to walk it back.

Pete Pachal (10:57)

And we’re reaping the winds of that with AI now that you think about it. But anyway, go on.

Paul Gewuerz (11:05)

Not that anybody knew that at the time. I don’t want to discredit anybody.

But what I’ve seen is that it’s a hard business to operate, especially where it’s needed most in rural America. I’m in western Colorado, in a town of 20,000, which is the biggest city anywhere around my region. I think a lot of folks on the coasts forget just how huge the country is.

It’s a very difficult business to operate on a smaller scale where it’s needed. If we’re using jiu-jitsu belt levels, it’s closer to the black belt level of business operations compared with something that has higher margins.

Combine that with the fact that many of the people who get into smaller outlets are mission-driven journalists. They want to serve the community. They’re not necessarily businesspeople.

You came up in media. There used to be a firewall between the business side and the editorial side. A lot of that needs to be dissolved, and people on either side need to think more like the other side.

Business operators sometimes come in and don’t know how to do good journalism. On the other hand, there are people whose organizations have fallen apart around them, and maybe they’re the last person left, a one-man or one-woman operation running the whole thing. They have to report on everything and get revenue coming in the door.

It’s a challenge. It’s a very, very complicated challenge. I think about it a lot every day, and I don’t have any great answers. But there are also amazing people doing amazing things out there.

Why Local Media Must Reinvent Itself

Pete Pachal (12:50)

For sure. The smaller the organization, the more everyone has to be mindful of how the business is doing and how you’re actually succeeding.

Neither of us means to disparage the spirit of the church-state separation, which has good roots in preventing business interests from affecting journalism. We both believe in that.

But at the same time, there has to be a strategy for running the business. If you’re News Corp, you might have strategists and executives making broader strategic decisions. But if you’re a team of three, four, or five people, everything is strategic to some extent.

I’m not at all endorsing commercial interests affecting the actual journalism, but when it comes to the broader directions you take, everyone is going to have a voice. Especially today, almost every decision seems a bit existential.

Paul Gewuerz (14:25)

Yes, very much.

The way I think about it sometimes is that the local news industry has gone the way of the music industry.

The big record companies in the ’60s, ’70s, and ’80s were absolutely printing money with records, cassettes, and CDs. Then the internet came along and democratized everything. Napster and LimeWire arrived, disrupted the business model, and now it’s a very different, much smaller business that’s much more spread out.

I think the same thing has happened with news.

Newspapers had this amazing business model throughout the 1900s. They had classified ads and were the primary source of advertising revenue. Then the internet came along, along with Google and Craigslist, and upended that.

It’s never going back to the way it was. Things evolve. They’re constantly in flux. It’s going to change, and it’s a matter of learning how to deal with that and adapt to the new realities and the new environment.

Pete Pachal (15:58)

The music analogy is interesting because the music industry was forced to figure out that selling songs for 99 cents, at least in the 2000s, was kind of the future. Then they had to adapt to this new business model, and it’s interesting that it was forced upon them by tech.

There are a lot of parallels here. I wonder about the media and strategic planning back then. Classified revenue was substantial, and then it went to zero. If they had planned around that, could it have made a difference?

Because in today’s media, specifically with AI, there’s a lot of strategic planning around Google Zero. It hasn’t happened yet. Obviously, Google isn’t dead as a search engine, and the 10 blue links still exist, at least for a while. But people have been planning around Google Zero for a while.

If people had started planning around classified zero in 2000, would there have been quite the apocalypse there was? I don’t know.

At this point in 2026, media has learned so many hard lessons over the last couple of decades that we’ve got this ingrained survival instinct now.

Are you seeing evidence of that at the local level? How are they surviving?

Trust, Community, and New Business Models

Paul Gewuerz (17:30)

To be honest, there are a lot of organizations that, in my opinion, have not changed enough. They’re still relying on advertising and sponsors, scraping by, and doing what they’ve always done.

But the ones that are thriving are doing something unique. They’re building a local brand.

You came on my podcast and talked about how you think it’s going to be a huge boon for PR firms over the next couple of years. Anybody who can generate trust and reliability in an age when anyone can produce anything with AI has an opportunity.

If you can build a brand, get people excited, and generate that trust in a community, those are the organizations doing a really good job.

I thought of a few more examples. There’s the Big Bend Sentinel in Marfa, Texas.

Max Kabat, who came on the podcast, and his wife moved to Marfa. There was an elderly couple running the Big Bend Sentinel, the local newspaper and print shop, and they wanted to retire. Max and his wife purchased it from them.

There was a huge print shop in downtown Marfa, but they didn’t need that much space anymore because most everything is digital now, even though they still have a print product.

They basically cut the space in half. They turned half of this old, really cool print shop into a café, community space, event center, and arts center, with the profits feeding into the journalism.

It’s become an absolute hub. Marfa is a town of about 2,000 people, and I think the combined café, event space, and newspaper employ around eight or 10 full-time people now.

There’s a similar example up in Maine. They have a café and were featured on CBS Sunday Morning. There’s also a bed-and-breakfast tied to it, and upstairs is basically the newspaper.

It all feeds into this idea of a community center. People who want to air their grievances about the city council can come down, have pancakes, and talk to journalists.

There are cool things like that happening.

Pete Pachal (20:18)

Is that an opportunity for sponsorships and things like that? Having an event space…events are the future for media broadly. Obviously, it’s one of many business models, but it’s a growing one.

It sounds like this could be a doorway to that at the local level. You could have a sponsored night and do something related to your publication.

Paul Gewuerz (20:45)

Yeah. My friend Paul Myers is in California’s Central Valley, and they do what I think are called “Brews and News” nights every month or quarter.

They basically rent out the local microbrewery, and you get one free pint of beer. The price is your email address for their newsletter list.

It’s not necessarily a sponsored thing, but it’s about subscribers and growing the audience. I think it’s a cool idea.

How Local Newsrooms Are Actually Using AI

Pete Pachal (21:18)

That’s really cool.

So, Paul, we’re about 20 minutes in and we haven’t talked about AI yet. I feel like I’m getting someone in my ear insisting that I get to the machines.

You talked about some success stories. How much AI is actually being used at the local level, and what are some of the most interesting use cases you’ve come across?

Paul Gewuerz (21:57)

There are a couple of things that almost everyone who comes on the podcast mentions.

The specific tool that seemingly every journalist and entrepreneur running a local news operation mentions is Otter.ai, which is a transcription service. It seems simple and obvious, but everyone swears by Otter for transcribing meeting notes, interviews, city council meetings, etc.

Another trend I’ve seen is normal old ChatGPT being used for ideas. Almost nobody, I should say, is using it to actually write content, at least not unchecked. But using it to generate headline or title ideas seems to be very popular.

I’m an optimist. I’m a fan of AI. I think it can be used as a tool.

A lot of local news publishers are scarred from the rise of Google, the internet, Craigslist, and everything else we’ve talked about. These big tech companies came in and basically hollowed them out over the last 20 years.

I think a lot of them view AI as an extension of that: “This is going to be the final blow. This is it. This is going to do us in.”

I fundamentally disagree with that.

As opposed to The Empire Strikes Back, I think AI tools are Return of the Jedi. I think they’re going to enable so much more time for these organizations.

There are boring back-end business use cases and tasks nobody wants to do but that need to get done. AI can reduce newsroom time spent on those things and enable more good reporting to get done.

I also think there are business models that local media operators have tried in the past that are going to become more possible now. For instance, the idea of operating as a local news outlet and also as a marketing firm for local businesses.

Some people have had success doing marketing for local companies. But that’s almost like adding a whole other business to your newsroom.

Pete Pachal (24:37)

Can you double-click on the marketing part of that? Are you talking about a publication with a team that might also do branded work?

Paul Gewuerz (24:46)

Yes. It’s something that’s been floated around in the space for probably the last 10 years, with some success. But once again, it’s a hard business to run, and that adds another layer of complexity on top of everything else.

Pete Pachal (25:02)

That speaks to what I was saying earlier about the church-state separation. At a major publication, obviously you’re going to have different teams and completely different operations.

At the local level, you’re going to have to put on different hats and figure it out. That’s just the reality.

Paul Gewuerz (25:17)

Yeah. For example, I’m mostly a one-man show for my business, and I need to get a new landing page up for a segment of LocalPod.co.

A year or two ago, that would have taken three days or, if I’m being honest, a week of my time to get polished. I can do that in half a day now with some of these AI tools.

It’s hard to overstate how much more efficient AI has made me at operating my business. I think that’s going to translate to local media operators.

For the marketing example, I think they’ll be able to do their reporting and still have enough time to take on clients, like the real estate brokerage in town that wants branded work done, while also getting a spot in the newspaper that week.

I think it’s going to create more options. We don’t know exactly what it’s going to enable, but I’m seeing it in my own business and my own tinkering with these tools.

There are all kinds of things possible now that I simply didn’t have the time or bandwidth to take on before.

What Can We Do Now That We Couldn’t Do Before?

Pete Pachal (26:26)

I like that. It’s making good on the promise that AI isn’t just about efficiencies. It’s not just making you a little faster, or even a lot faster, and hopefully getting time back.

It’s also about asking: What can we do now that we simply couldn’t do before?

Branded content isn’t reinventing the wheel, but for these publications where, as I said, everything is existential, that’s a big move. Now they don’t necessarily need to hire a completely different team and buy a whole different set of software to do it.

That feels like progress to me.

What also resonated with me is that a lot of the distrust of AI stems from its effect on distribution. AI is obviously vastly affecting distribution and digital discovery. That’s indisputable. But its use as a tool is also indisputable.

You can acknowledge how good it is at making certain things better in your workflows while also acknowledging that, yes, it’s doing something strange to audiences as people get AI summaries and stop there.

Broadly, it’s a “don’t throw the baby out with the bathwater” argument. But I feel like that’s where journalists often end up for some reason.

Are you seeing that change as AI becomes more embedded? On my end, over the last five or six months, I’m seeing more of a resignation among skeptics that this is happening.

Paul Gewuerz (28:23)

I’ve felt the exact same way.

A year ago, if I’d seen some AI headline in the local news industry about somebody using it for something, there would have been a ton of backlash, shaming, and people piling on.

But over the last five or six months, I’ve seen a marked shift in the mood of the industry.

Whether people are resigning themselves to it or just getting more familiar with AI, realizing what it can and can’t do, and becoming more aware of it, the mood has changed.

The vibe has shifted, Pete, from what I can tell.

Could AI Actually Strengthen Local News?

Pete Pachal (29:01)

Yeah. Not completely to, “Hey, it’s awesome,” but more to, “Okay, this is getting embedded.”

Let’s talk about AI disintermediation and distribution. Do you have a sense of the unique factors affecting local media?

Intuitively, I would think local media might be a little less affected because you’re more invested in your own community and what’s happening there. You’d want to go directly to the source.

What are you hearing about how badly Google Zero or the traffic apocalypse is affecting local media?

Paul Gewuerz (29:50)

I think in terms of trust, it’s actually a really good thing for local news.

People are inundated with content coming at them now. If there is a trusted local voice, I think people are going to turn to that more and more. There’s that human connection, a human byline they can actually read.

That being said, local media operators still need to pull that off. It goes back to what I was talking about before: brand building and trust building.

Not everybody has that down.

A lot of people I talk to honestly think they can keep doing what they’ve always done. “We’ve got our website up. We’ve had our masthead for 50 years. People trust that.”

It’s just not the case anymore.

You still need to be on social. You need to be everywhere at the same time.

It’s a dance between the people who don’t want to change and the people who are changing. The people who get it and recognize the opportunity realize that I think it’s going to be a good thing.

Because the AI slop out there is ridiculous.

AI Slop, Fake Local News, and “Pink Slime”

Pete Pachal (31:08)

Let’s talk about slop specifically for local media.

Every few months, it feels like there’s some kind of story about someone trying to game the system with local news.

There was a guy who was eventually hired by 6AM City. That wasn’t necessarily malicious. I think there was a mix of people trying things out who aren’t really journalists and are just throwing locally oriented content out there.

Then there’s this more recent thing in Florida involving a sort of fake site, which sounds a little shadier, and they were apparently running a whole bunch of other sites.

I feel like this keeps happening in local media. Maybe it’s because people think they can do something with local sites and stay under the radar, as opposed to trying to create some fake national site that probably wouldn’t get very far.

Is that basically what’s happening, or is there some unique perfect storm of circumstances fueling this?

Paul Gewuerz (32:31)

I think that’s definitely a thing. You see those headlines pop up.

I think it’s two different things.

One is more malicious, like the story in Florida. It’s referred to as “pink slime.”

Pink slime sites are basically websites that look like legitimate news operations but are funded by some kind of organization with a specific goal, usually political operatives or something like that.

They’re playing themselves off as reliable local journalism and then slandering one political party or the other party’s candidates.

So that’s happening, often with strange funding that nobody can really trace.

At the same time, there’s been a huge trend I’ve seen on YouTube and some podcasts of people getting really interested in local newsletters specifically.

There have been some huge success stories where people say, “I run this local newsletter, and now I make $400,000 a year.”

That has happened, and there’s been a lot of interest and content popping up around it.

With the rise of AI tools making things easier, there are also a lot of people in their basements throwing spaghetti at the wall. Someone can spin up 15 local newsletters with almost nothing, ripping off actual local news outlets, copying their work, and putting it out there.

I think those are the two main culprits.

But there are also legitimate people creating local curated events newsletters. It’s not as simple as good and bad. There are quality people doing this work.

My friend TJ Larkin is in that space, and he puts out a really quality product and teaches other people how to do it.

Podcasting as a Growth Strategy for Local News

Pete Pachal (34:29)

Absolutely. Let’s switch gears as we wrap up here because we’re both podcasters, and you’ve obviously talked and written about podcasting and its relevance to local media.

Where does podcasting factor into a local news strategy? Obviously, people like podcasts, but they’re harder to scale. Is that less true now?

What’s a good podcast growth strategy for local news in 2026?

Paul Gewuerz (35:02)

That’s one of the reasons I zoned in on this a few years ago.

Podcasts are notoriously hard to reliably grow. And when they do grow, it’s almost hard to figure out why unless there’s some kind of viral moment.

If you start a podcast about World War II in the Pacific Theater, for example, it’s hard to find audiences. It’s hard to find first-party data.

The difference I’ve seen with local podcasts in particular, although this does take a little bit of a budget, is a site I use called AudioGO.

It’s an advertising platform that allows you to create 15- and 30-second audio ads and place them on top podcast networks, Pandora, and a few other platforms.

The key is that you can geotarget them by ZIP code.

I’ve seen some success with this, and it’s particularly useful for local podcasts.

If you can communicate your message well in a 30-second spot, something like, “Hey, this is the Montrose Daily Press podcast covering the news and events in your town,” you can geotarget that to people listening to The Daily or top true crime podcasts in your local area.

I haven’t seen anything else work as well as that kind of strategy for general podcasting.

Your podcast and my podcast don’t work like that. You’re covering AI, I’m covering local media, but we’re both speaking to the whole country. It’s harder to target those people.

That’s the edge I’ve seen. Any local operators listening should feel free to use that. That’s kind of the secret sauce we’ve been using.

What Keeps Paul Up at Night About AI?

Pete Pachal (37:00)

I’m sure everyone’s got their notebooks out right now.

I try to end these conversations with a similar question because we see divergent futures ahead of us with AI involved. There’s going to be bad, and there’s going to be good.

What is something that might keep you up at night with regard to AI and media? And what’s something you’re hopeful about?

Paul Gewuerz (37:29)

Something that keeps me up at night is the relentless pace of change.

It’s really hard for me to see what anything is going to look like in two or three years, let alone six months from now.

I’ve been over the moon with some of the capabilities I have now, like with Claude Code. I’ve been working on a software platform for my company for two and a half years and had about 10% done.

I finished it in the last two months.

It’s operational. People are on the platform.

Pete Pachal (38:00)

Nice. What’s the platform? Tell me about it.

Paul Gewuerz (38:03)

It’s my LocalPod Studio. It’s basically a dashboard studio where you can turn written content into an AI-narrated podcast that’s fully distributed in a couple of clicks.

Anybody who wants to check that out can go to LocalPod.co or message me.

But the thing that keeps me up at night is that I built this…

Pete Pachal (38:18)

Nice. Beautiful.

Paul Gewuerz (38:28)

It’s pretty incredible.

I have a little bit of coding ability, but not much. Minor league. And I’ve been able to build this crazy thing, and I have all these other ideas I can build.

But at the same time, I’m thinking: That means anybody can build this.

I think it’s a great equalizer and a great democratizing force. I’m excited and optimistic that I can build things and do things for my business.

The competition is going to come with that. I think it’s still early.

Combine all that with the fact that I don’t know what the whole economy is going to look like in a couple of years because you can’t map what that growth is going to look like.

I’m sorry, what was the second part of the question?

Pete Pachal (39:08)

You kind of almost mixed it in there, but it was also: What are you hopeful about?

Paul Gewuerz (39:25)

It’s really kind of the same thing.

There are doomers. There’s a lot of doomerism around AI. I don’t think AI is sentient. I don’t think it’s going to get there.

When you actually dig in and see how it works, it’s a very powerful tool. I don’t think it’s going to murder all of us. I just don’t see it in the cards. Or there’s a very small chance, at least.

Pete Pachal (39:36)

Yeah, people can tell it to do bad things, but it doesn’t have any ideas of its own.

Paul Gewuerz (39:39)

Yes. There’s no ghost in the machine, is my take on it.

I think this is a new industrial revolution. I don’t think that’s underselling it at all.

People are worried about all the jobs disappearing. But every time people have said that in recorded history, if you go back and read about it, new things emerge that people couldn’t even imagine becoming jobs.

I graduated high school in 2003. I’m 41 years old.

My job titles today include podcast producer and SaaS platform owner. My wife and I also operate an Airbnb upstairs.

None of that existed when I graduated high school in 2003.

If I’d said I was an Airbnb host and podcast producer, I would have been locked up, basically. And that was only a little over two decades ago.

Things change.

I think there’s a future of abundance, and I think AI is going to help us unlock that. There are some issues with it, but I think they’re going to get sorted out because it’s worth it to sort them out.

Pete Pachal (40:50)

That’s awesome. We’ll leave it there.

Paul, thank you so much for dropping by The Media Copilot and sharing your thoughts.

Paul Gewuerz (40:55)

Yeah, this was fun, Pete. I always enjoy these talks. It gets me fired up. Thanks for having me. I appreciate it.

Pete Pachal (41:01)

Cool. We’ll do it again soon.

The post AI didn’t kill Local News. Could it actually save it? 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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In 2023, a company called Alabama Community News LLC supposedly spent $3.2 million to buy 47 weekly newspapers across the state. The corporate owners fired the local staff, replaced them with an artificial intelligence system that scraped high school sports scores, and promptly drove the entire network into bankruptcy. The story even named a specific 26-year-old campaign staffer who generated 70 percent of the copy.

None of it actually happened. The entire 1,900-word saga was a fabrication published by a site called The Editorial, according to an investigation by Nieman Lab. The targeted newspapers, including the Shelby County Reporter and the Centreville Press, are still printing. The angry local advertisers quoted in the piece do not exist. The story falsely claimed the roll-up was funded by 1819 News, a real conservative outlet in the state, adding a layer of plausibility to the hoax.

The fake story gained traction among journalists on social media platforms like Bluesky before the operators pulled it down. They replaced the page with a sterile retraction notice citing “fact-verification concerns.” But the Alabama hoax was not an isolated incident.

The Editorial has built a bizarre subgenre of AI-generated obituaries for real American newspapers. The site previously published fabricated stories detailing the collapse of the Chattanooga Times Free Press, the Kenosha News, and the Macon Telegraph. The nonexistent reporters credited with these stories sport fake resumes claiming past stints at ProPublica and Reuters.

The motive behind the site remains murky. Domain registration and payment records point to a Finnish technology company called Nordiso Group, which develops AI study apps. Yet the site’s political sections suggest a different angle. The Editorial publishes a high volume of geopolitical content focused on Taiwan and the South China Sea, heavily pushing narratives that highlight Chinese military dominance.

These geopolitical stories share obvious synthetic fingerprints. Nearly every piece opens with a variation of the exact same scene: a nondescript, windowless conference room where a secret document slides across a table. This repetitive structure aligns with tactics tracked by groups like the Stanford Internet Observatory, which monitors state-sponsored disinformation campaigns. It also highlights how cheap synthetic media allows operators to flood niche topics, a trend we track closely at The Media Copilot.

For publishers, this represents a strange new vector of reputational risk. Newsrooms are used to fighting disinformation about elections or public health, but now they must monitor for synthetic hoaxes about their own business operations. A fake story about a newspaper shutting down or firing its staff can spook actual advertisers and confuse real subscribers before the publisher even realizes the rumor exists.

The barrier to generating convincing local news copy is gone. Operators no longer need to understand the nuances of a community to write a plausible story about it. They only need a prompt and a target, leaving local editors to clean up the mess when the synthetic fallout hits their own backyards.

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Google’s AI-generated search summaries are frontlining details about the recent suicide of a public figure at the top of search results, raising concerns that AI products are amplifying sensitive information that traditional news guidelines advise against publicizing. 

This incident highlights a growing challenge for AI search products as they determine not only what information is relevant, but what deserves emphasis. 

Nicola Agius, CEO of the newsbranch Reach, was the first to identify the issue on June 30 after seeing an Instagram post stating that the brother of Caroline Flack, a former presenter for the U.K. reality show Love Island, had died. Agius searched for “Caroline Flack brother” on Google to verify the news.

Not only did Google’s AI Overview confirm his death, Agius shared that it prominently stated that he had died by suicide, also including the location, method and the effect on his body. The summary also detailed how he was found. 

The Media Copilot was able to replicate the result on July 3. Using the same search query, Google’s AI Overview stated the method of death alongside all facts detailed above. The search was conducted by a reporter in Europe. Results from searches in the United States, however, did not always show the details of his death. 

The Media Copilot also found that even when Google’s AI Overview does not initially surface details of a suicide, simple follow-up prompts can elicit extensive information, including the method, location, police reports, descriptions of the scene and other sensitive information. 

The content displayed by Google’s AI Overview originated in publisher reporting, but they were given greater prominence in the AI-generated summary than in the source articles themselves. In some cases, the information was even removed from the original reporting altogether. One publication cited by Google for the cause of death no longer included those details in the linked article by July 3, while other outlets continued to reference them but with less prominence. 

The Associated Press guidelines on reporting suicide urge publishers “to not go into detail on the methods used.”

“It is incredibly worrying—and disappointing—to see some press sovereign and AI overviews including references to methods of suicide,” said Lois Sparks, head of safeguarding at Mind, a mental health charity, in an interview with Press Gazette. “Not only can these AI summaries be incredibly triggering for people to see so prominently, but in some instances, there is little signposting to mental health support.”

The Media Copilot’s inquiry did trigger a support message, which stated: “If you or someone you know is struggling, support is available in the U.K. by calling the Samaritans at 116 123 or texting SHOUT to 85258. In the U.S., you can call or text the Suicide & Crisis Lifeline.”

“Broadly, AI overviews give an illusion of definitiveness,” Sparks said. “They make highly sensitive and nuanced areas seem like concrete facts. If AI overviews are covering news, then they must be regulated to the same standards of traditional media—which they are currently not.”

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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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For the second time this year, Google has removed and then reinstated a Press Gazette investigation into Clickout Media from its search results after an anonymous complaint under the U.S. Digital Millennium Copyright Act, restoring it only after the outlet pressed for comment. The delisted article reported that theU.K.-based marketing company had published AI-generated news stories containing factual errors and fabricated information.

The Press Gazette piece, published last week under the headline “AI reporters churn out error-strewn stories for football websites,” reported how Clickout Media acquired three established U.K. football news websites and began publishing stories under AI-generated reporter bylines that Press Gazette found contained numerous errors.

According to records in the Lumen transparency database, which publishes DMCA takedown notices Google receives, an entity identifying itself as “DRF Corp” accused Press Gazette of “willfully” copying its content and images. The complaint claimed the original work was a now-deleted Reddit post. Press Gazette said the allegedly infringing content was unrelated to its investigation. 

The latest takedown follows a similar incident in March, when Google removed a Press Gazette investigation into Clickout Media from its search results after another anonymous complaint. The article reported that the company had acquired news websites to drive traffic to its promotion of online casino content. Google reinstated the story after Press Gazette sought comment. 

The second article has since been reinstated as well after Press Gazette pressed Google for comment. But the pattern of the same target, the same anonymous complaint and a reversal by Google when challenged has drawn criticism from media industry figures, who say bad actors can exploit copyright takedown systems to remove legitimate reporting from search results while low-quality AI-generated content remains visible. 

Dominic Young, chief executive of the micropayment firm Axate and a co-founder of the SPUR Coalition on AI licensing standards, condemned the takedowns in comments posted on LinkedIn. 

“By effectively rendering copyright infringement consequence-free, and reserving the right for tech platforms to profit from it, this law created anarchy online and made copyright infringement into a business model – now being exploited by AI companies and a swarm of proxies helping them get whatever they want, regardless of what the owners say,” Young said. 

The DMCA allows anyone to file a takedown notice regardless of whether they have registered their work with the U.S. Copyright Office. Google reviews each notice to ensure it meets legal and policy requirements. It is not required to remove the reported material, but failing to act on a valid notice could expose the company to secondary liability for copyright infringement, so it usually complies. 

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Cloudflare said Wednesday it will begin blocking AI training and agent crawlers by default on ad-supported websites, a change that could force companies such as Google, Apple and Microsoft to more clearly separate search indexing from AI training if they want continued access to large parts of the web.

The policy, scheduled to take effect Sept. 15, applies to new Cloudflare customers, new sites added by existing customers and existing Free-tier customers who have not changed their settings. Search crawlers will remain allowed by default, but training and agent crawlers will be blocked on pages that display ads.

The company said the changes are designed to help publishers remain visible in AI-powered search results while preventing their content from being used for AI training or autonomous agents without permission or compensation. 

“Now that the majority of traffic is non-human, we must go further and act faster so that a sustainable ecosystem can emerge,” said Matthew Prince, Cloudflare’s co-founder and CEO. 

Splitting up mixed use crawlers

The Web giant said bots that combine search, AI training and agent activity—known as mixed use crawlers—without letting site owners choose among those uses will be blocked on ad-supported pages when training or agent access is blocked. In a company blog post, Cloudflare named Googlebot, Applebot and BingBot as multi-purpose crawlers that could be affected by the most restrictive applicable rules.

“We hope that our proposed default changes encourage mixed use crawlers to separate out search from agent use and training,” Prince said. 

Cloudflare said customers will be able to manage three categories of AI traffic: Search, which indexes content for later retrieval; Agent, which accesses a site on behalf of a user in real time; and Training, which collects content to train or fine-tune models. The controls are available to all Cloudflare customers, including those on the Free tier.

That distinction matters for smaller sites. A spokesperson for  Cloudflare said the new controls are intended to give all website owners more options for managing AI traffic, not only publishers with ads or subscriptions. But the default blocking policy is tied to pages with advertising, and Cloudflare’s compensation plans remain focused on commercial use cases where AI systems access or surface publisher content.

Alongside the new crawler controls, Cloudflare is expanding analytics to show publishers how bots interact with their content and how much traffic AI platforms send back. The company is also pushing into what it calls Answer Engine Optimization, or AEO, offering tools it says will help customers understand how often their content is cited or surfaced in AI-generated answers.

Cloudflare also announced efforts to reduce unnecessary AI crawling. According to the company, more than half of AI crawler traffic is spent repeatedly checking web pages that have not changed. Because Cloudflare sits between websites and online traffic, it says it can signal to AI companies when pages have been updated and worth revisiting. The company said it is testing those signals with AI firms and plans a broader rollout later this year. 

New compensation model

The company is also expanding its publisher compensation strategy by evolving its Pay Per Crawl program into a new system called Pay Per Use. Rather than paying publishers when content is crawled, the new model is designed to compensate them when their content is actually used in AI products. Cloudflare said it is working with AI companies including Ceramic.ai and You.com on the initiative. Under the arrangements, publishers could be paid when their content appears in AI search results or when AI agents access premium content on demand. 

But the model does not yet answer the hardest compensation question: what happens when a publisher’s work is used for model training but never appears in a cited answer? Asked whether Pay Per Use compensates publishers in that scenario, The spokesperson said the program is aimed at “programmatic, real-time access and discovery,” and described Pay Per Crawl and Pay Per Use as only two possible economic frameworks.

“The digital landscape is evolving rapidly,” said Marrissa Holloway for Cloudflare. “We welcome ideas from publishers, creators, and AI companies alike on how to build a thriving agentic Internet.”

Holloway did not directly say what Cloudflare’s cut of any revenue generated would be. “It has always been our philosophy that our customers derive many multiples of value more than they pay us,” she said.

The Media Copilot’s take

Cloudflare is not solving AI compensation for the whole Web. It’s building a bargaining layer for larger publishers with enough traffic and revenue to measure, block and negotiate. That helps the larger content outlets, but smaller sites and independent publishers will get switches to turn on and off. That’s useful, but switches don’t mean they have leverage. The long tail of the Web—the indy blog sites, community web pages and hobby sites—can say “no” more clearly, but there still no obvious way for them to get paid when their work is used for an AI’s training data and never comes back with a citation or link.

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Anthropic restored global access to Claude Fable 5 on Wednesday, one day after launching Claude Sonnet 5, closing out an 18-day export-control suspension that had cut off both of the company’s newest flagship models since mid-June. Anthropic also restored access to the more powerful Mythos 5 model for a set of U.S. organizations approved under its Glasswing cybersecurity program.

The back-to-back announcements come at a sensitive moment for the company. Increasingly more capable models, like OpenAI’s GPT-5.6 family, draw heightened scrutiny from the U.S. government over national security and cybersecurity concerns. 

The suspension began June 12, when the U.S. government applied export controls to Fable 5 and Mythos 5 after Amazon researchers reported a technique that allowed Fable 5 to identify software vulnerabilities, and, in one case, produce code demonstrating how to exploit one. 

Anthropic said it had found that several less-capable models, including Opus 4.8 and OpenAI’s GPT-5.5, could replicate the same behavior, and that the bypass didn’t expose any capability unique to Mythos 5. The company nonetheless spent the following weeks working with the government to fix it, training a new safety classifier that it says blocks the specific technique “in over 99% of cases.”

Anthropic acknowledged the new classifier could flag more benign coding and debugging requests as it errs toward caution. Newsrooms and media teams running Claude Code or agentic research workflows should expect occasional false-positive refusals on security-adjacent tasks going forward.

Fable 5 is rolling out today across Claude Platform, Claude.ai, Claude Code and Claude Cowork. Pro, Max, Team and select Enterprise plans can access it for up to half of their weekly usage limits through July 7. Then, access will shift to usage credits. 

Sonnet 5, which Anthropic launched late on June 30, is Anthropic’s middle ground between capability and safety. According to the company, the model performs close to the former top model, Opus 4.8, but at a cheaper price: $2 per million input tokens and $10 per million output tokens. The price will rise to $3 and $15, respectively, on Sept. 1.

Security concerns

But the same capabilities that make AI systems more useful can also make them more dangerous, raising questions about whether safety measures are keeping pace with rapidly advancing model performance.  

In its announcement, Anthropic sought to reassure users that Fable 5 has adequate security measures and Sonnet 5 poses a relatively low risk. The company said the Sonnet model is better than its predecessor at refusing malicious requests and resisting prompt injection attacks, a common technique used to manipulate AI systems into bypassing their safeguards. 

However, Anthropic says that while Sonnet 5 exhibited fewer undesirable behaviors overall than Sonnet 4.6, it also showed higher rates of misaligned behavior than both Opus 4.8 and Claude Mythos Preview, which have stricter safety controls. 

“Sonnet 5 was never able to develop a full working exploit, but it does show a slightly higher rate of partial success than Sonnet 4.6. This latter change is likely due to improvements in general intelligence rather than specific training,” Anthropic said in its press release. 

These results suggest that improvements in general reasoning and problem-solving abilities may also increase the model’s capacity to assist with offensive cyber activities, although the company emphasized that Sonnet 5 was unable to develop a complete exploit for Firefox vulnerabilities.

The findings reflect a broader concern among governments and security researchers: AI models do not necessarily need specialized cybertraining to become more useful to attackers. As reasoning and problem-solving abilities improve, models may naturally become more effective at identifying vulnerabilities, generating attack strategies and assisting with technical exploitation.

“Because we judged that the overall level of cybersecurity risk from Sonnet 5 was low, the safeguards are less strict than those launched with Fable 5, which block a much wider range of cybersecurity tasks,” Anthropic said. 

The company said Mythos 5, which is still restricted to a small set of companies and organizations in Glasswing, “can be used to find and exploit software vulnerabilities more effectively than any other model — and all but the most skilled human security experts.” 

Fable 5, however, launched with what Anthropic called the strongest safeguards it has ever applied to a model, after doubling its safety research staff in the month before launch. Researchers from the Commerce Department’s Center for AI Standards and Innovation tested both the original and updated safeguards and, Anthropic said, “agree that they are extraordinarily strong.”

U.S. government collaboration

With the lifting of the Mythos and Fable restrictions, Anthropic is further deepening its cooperation with the U.S. government. This marks an abrupt turnaround for the company, which has had a turbulent relationship with the Trump administration since the Pentagon labeled the company a supply chain risk in late February. The feud arose over Anthropic’s opposition to the use of its Claude models for mass domestic surveillance or fully autonomous weapons systems. 

Reuters reported that Commerce Secretary Howard Lutnick said in a letter sent to Anthropic that the company would work with the government on safety protocols for Mythos, Fable and future models, and to disclose any malicious activity it detects. However, Lutnick warned that the department “reserves the right to reevaluate the decisions made in this letter and the necessity of reimposing a license requirement, should circumstances change or should Anthropic fail to adhere to its commitments.”

“Our hope is that this collaboration … will serve as the basis for systematic rules for the whole industry,” Anthropic said in its Fable 5 release, “and even offer the beginnings of a template for effective global coordination on the risks and benefits of AI.”

The company is currently appealing the supply-chain risk designation in the D.C. Circuit Court of Appeals. It’s unclear how today’s announcement will affect the suit.

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OpenAI said that it plans to give a select group of government-approved partners early access to GPT-5.6 Sol, its most powerful AI model to date, before releasing the product more broadly. The limited rollout follows a request from the Trump administration, which asked the company to adopt a phased launch strategy for its next-generation AI system while security reviews are conducted. 

Last week’s request came from the White House’s Office of the National Cyber Director and Office of Science and Technology Policy, which have pushed AI developers to give federal agencies early access to frontier models so officials can evaluate their capabilities and potential security risks before wider deployment,

OpenAI CEO Sam Altman told employees in a memo that GPT 5.6-Sol will initially be available to 20 approved partners, including Amazon’s Bedrock platform. According to the memo, access is being granted on a “customer-by-customer” basis while the review process is underway. 

“We’ve made clear to the U.S. government that this is not our preferred long term model, and will work with them and others in industry to achieve a more sustainable approach for future releases,” Altman said in the memo. Altman said he hopes to release GPT-5.6 to the public a “couple of weeks later.”

The decision reflects growing concern over what the Trump administration says are legitimate national security implications of increasingly capable AI systems. 

OpenAI says GPT-5.6 Sol is its most advanced model to date, with improvements in reasoning, autonomous task execution, software engineering and cybersecurity-related capabilities. It released benchmarks that said its performance was broadly comparable to Anthropic’s Mythos 5, which was withdrawn on June 12 following a directive from the Commerce Department expressing concerns that its advanced capabilities could create new cybersecurity threats. 

At the time, Anthropic said the concerns were over “a small number of previously known, minor vulnerabilities” and that “other publicly-available models are able to discover them as well without requiring a bypass.” 

Politico reported that the initial vulnerability was brought directly to the White House by Amazon CEO Andy Jassy. (Amazon is an investor in Anthropic.)

“We have reviewed a report that we believe is the basis of the government’s directive and validated that the level of capability displayed there is widely available from other models (including OpenAI’s GPT-5.5), and is used every day by the defenders who keep systems safe,” Anthropic said in its statement.

Anthropic and the Trump administration have been sparring for months since a dispute over how Anthropic’s models would be used by the Pentagon. Reports that the company’s models were used in U.S. operations in Venezuela in January, in violation of its licensing terms, which prohibit using Claude models to commit violence, led to a near complete rupture. 

Adding further layers to the drama, just hours after OpenAI announced the limited rollout of GPT-5.6, Anthropic disclosed that the Trump administration had approved a limited release of Mythos 5, reversing the Commerce Department restriction. 

The restrictions on GPT-5.6 Sol and Mythos 5 follow a recent call from the Five Eyes alliance for closer coordination on advanced AI development and security. A White House official said the administration continues “to collaborate with frontier AI labs to develop shared approaches for addressing the challenges of scaling this technology.” 

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