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.

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.

The post AI accuracy is Google’s problem—until it becomes a publisher’s appeared first on The Media Copilot.

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.

The post Cloudflare’s new plan could change how AI pays publishers appeared first on The Media Copilot.

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

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

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

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

What the click pattern tells you

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

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

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

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

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

Trust converts but not on impact

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

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

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

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

Stop playing defense

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

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

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

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

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

A version of this column appears in Fast Company.

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

This episode is sponsored by: Adobe Acrobat

This week on The Media Copilot, Pete Pachal sits down with Taneth Evans, Head of Digital at The Wall Street Journal, to explore how one of the world’s leading news organizations is navigating the AI revolution.

Rather than chasing every new AI trend, Evans shares how the Journal evaluates emerging technology through a simple lens: Does it genuinely help journalists do better work or help readers better understand the world?

From AI-powered investigative tools and newsroom workflows to personalized storytelling and adaptive content, Evans offers a thoughtful look at how AI can strengthen journalism without compromising trust.

“So many times in the past few years, I’ve said to people, what would you do with a building full of journalists at your disposal? No newsroom feels like it has enough resources… How can we use AI to help us get closer to the answers to that question?” — Taneth Evans

The conversation explores why journalism is evolving beyond a single article format into flexible experiences tailored to how each reader prefers to consume information, while keeping facts, reporting, and editorial standards at the center.

Sponsor:

The new Adobe productivity agent orchestrates tools and models to generate images, text and rich content like presentations, podcasts and social posts, while also powering conversational PDF editing in Acrobat.

With new PDF Spaces capabilities, users can combine files, links and notes into interactive, shareable spaces for research, collaboration and content creation. VICE News, Kid Cudi and celebrity event planner Mindy Weiss are already using these tools to build trust and deeper engagement with their audiences.

Link: Do that with Acrobat: AI-Powered PDF workspaces | Adobe Acrobat

What we cover

• How The Wall Street Journal evaluates new AI technologies

• Why audience needs come before AI innovation

• The rise of personalized and adaptive journalism

• AI tools transforming investigations and newsroom workflows

• How AI can create entirely new reader experiences

• Why trust, attribution, and media literacy matter more than ever

• The future of publisher owned experiences in an AI driven world

• Why great reporting becomes even more valuable in the age of AI

As AI changes how information is distributed, the challenge isn’t simply adopting new technology. It’s preserving trust while creating better ways for people to engage with journalism. Evans argues that the future belongs to news organizations that use AI to deepen their relationship with readers, not replace it.

Why this matters

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News consumption is changing rapidly. Readers increasingly expect personalized, accessible experiences while publishers face growing competition from AI powered search, chatbots, and automated summaries. The organizations that succeed will be those that combine trusted reporting with innovative experiences that make journalism more useful, more engaging, and more relevant. This conversation offers an inside look at how one of the world’s leading newsrooms is preparing for that future.

About the 👤 Guest

Taneth Evans Head of Digital, The Wall Street Journal

LinkedIn: https://www.linkedin.com/in/taneth-evans-b35877162/

The Wall Street Journal: https://www.wsj.com

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

TRANSCRIPT

Pete Pachal (00:25.442)

Hi, welcome to the Media Copilot. It’s a podcast about how AI is changing media, news, and communication. My name’s Pete Pachael. I cover tech for a long time as a journalist, and now I have deep conversations with the media people, the builders, and the creators, or are all answering the question how will we get information in the future? And how will that change the jobs in the industries whose business is information, especially media?

One of the biggest questions in media right now is whether AI will make journalism more useful or more generic. We already live in a world of apps and services that are trying to make information as convenient and efficient as possible. I think AI summaries, chatbots, personalized feeds, the list goes on. That creates a huge challenge for publishers, especially premium publishers. AI might be able to help you serve your readers better, but how do you do that without flattening the reporting or weakening the brand or worst of all, breaking trust?

My guest this week sits right in the middle of that question. Taneth Evans is head of digital at the Wall Street Journal, which is obviously one of the most important news organizations in the world. Her work touches everything from audience strategy to newsroom culture to the product roadmap, and that includes the journal’s approach to AI. The journal’s already moved forward with some AI-driven features, including AI summarized bullet points, some reporter tools, and even a bespoke chatbot that was specifically made for iPhone coverage.

But what I think makes the journal’s approach interesting isn’t just the tools. It’s the broader idea that journalism itself may become more flexible. The same reporting can turn into different formats, whether that’s summaries, explainers, audio, video, interactive experiences, or even something else, all depending on what the reader wants. So today we’re going to talk about how the journal thinks about AI, how a global newsroom with serious standards decides what’s safe to ship.

And what audience ac audiences actually want from AI-powered news products. I’m excited to get into it. quick note: if you’re listening on Apple or Spotify, please leave 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 if you don’t mind. Those things really do help more people find the show. All right, let’s get started. Taneth Evans, welcome to the Media Copilot.

Taneth (02:40.285)

Thanks for having me.

Pete Pachal (02:42.35)

it’s my pleasure. so I want to get into all that cool stuff, AI experiences, what everything you are in charge of there at the journal and and how it’s rapidly progressing along with AI. But I’d love to hear a little bit more about you and your background. So tell me a little bit about, you know, how you how you came to be at the journal, how your role has evolved there, and if in particular like how it’s evolved alongside AI.

Taneth (03:07.345)

Hmm. I’ve been at the journal for just over three years, three years in February. I arrived with the new editor-in-chief, Tucker, new at the time. I had worked with her previously in London, where she was editor of the Sunday Times, and I was lucky enough to be brought along for the ride when she came to the journal. And so it’s actually, although it’s been three years, feels like…

a lot longer because well humbly what we’ve achieved feels like more than three years work. We arrived and kind of set a broad newsroom strategy and spent a long time articulating that and making sure we had all of the resources and skill sets we needed to action it. And then as you say, think we were, know, AI technology was obviously very important three years ago and it was obviously going to become more important.

But I think we were all surprised, I certainly was, by the speed with which it started taking over lots of conversations, both in the workplace and beyond, really. And so it did become a larger part of my job very quickly. It’s interesting when you think about who should own these things in a newsroom. There’s so many things that come along with it. It’s technology, yes, but it’s also…

governance and strategy and to what end we will employ this technology. And so I think I was kind of in a very lucky position really to be the obvious person to have it sit within my team. But we very quickly spun up a working group in the newsroom that was led by our now head of data and AI, the wonderful Tess Jeffers. And I think that’s been one of the really important things

that we created that working group very quickly. Firstly, to talk about governance and guidelines and how we would speak to the newsroom about AI. But now it means that that group is on the forefront of discussing emerging technology and stress testing it and thinking about how it should look and work in the newsroom. And so nothing is handed to us. We’re very much on the front foot of creating those guidelines and

Taneth (05:28.765)

hopefully making it a little bit exciting and less intimidating too.

Pete Pachal (05:34.538)

So you this working group has sort of evolved into the main sort of filter, I guess, as new technologies come out and and new techniques sort of develop to that does it go through this this group and and what is that process like?

Taneth (05:51.504)

Yeah, there are representatives from all corners of the newsroom in that group. there are people from my team, the digital team, content strategy, audience development, all of that good stuff. There are also people who represent video, audio, all of the different formats, visual storytelling on the website. But then we also have representatives from standards and the investigations team and

reporters that are very active and interested in using the technology. And so it means that those discussions are lively and exciting and we really kind of drill into things very deeply. And I think that’s why it’s been successful. It’s full of people that are very curious and excited. And so we can, you know, look at a proposition or a new technology and very quickly say, but practically, what does this mean?

Pete Pachal (06:45.954)

Mm-hmm.

Taneth (06:46.552)

what end would we use this rather than, you know, talking about things.

Pete Pachal (06:51.554)

And how’s like how what is your filter when you approach that, right? Because you can think about it on across a number of dimensions. You can think about it like, you know, product and efficiencies. You can think about, you know, what the audience is it enhancing some kind of experience or just getting things to them quicker? you know, overall distribution, I’m sure that sort of factors in. and are there any like how how do you like to approach these and you know what within those filters, like

How do you sort of identify red lines and sort of like how where you would s definitely say no?

Taneth (07:24.731)

It’s interesting, isn’t it? Because people will often say, so what are you doing with AI? And it’s kind of, it’s too big of a question. What does, what does it mean? Because as you rightly said, it’s, it’s so many things. It’s a distribution layer, one that I think will fundamentally change, maybe has already fundamentally changed how consumers will act out there in the wild. It’s internal tooling, yes, efficiencies, but also

things to make us more powerful. It’s experience. It’s, know, ways that we might augment our products and offer net new things to readers. And so the first, for me, the first layer we have to go through is, this a need? Does this meet a need? Does it meet a need that we have in the newsroom or does it meet, crucially, one of our audience needs? Because I don’t, I’m not so interested in doing things that

will be cool and that no one will look at. I love doing cool things, don’t get me wrong, but I want people to see it. I want it to be for a reason, you know? And the other, I suppose big thing that I try to advocate for is in the newsroom, I want us to think about using AI for net new, for powerful things. I already alluded to it, but efficiencies are great and we all need them, but I’m…

less interested in them. I’m really, really interested in the stuff that we can do that we wouldn’t be able to do before. So many times in the past few years, I’ve said to people, what would you do with a building full of journalists at your disposal? No newsroom feels like it has enough resources. I mean, ever, but particularly now. How can we, how would we answer that question? And how can we use AI to help us get closer to the answers to that question?

Pete Pachal (09:06.722)

Right.

Pete Pachal (09:18.146)

Nice. So what what would you say is been net new? Like what what has excited you the most over the past couple of years and where do you see that particular aspect, not just the efficiencies, but the the new stuff? Where’s that going?

Taneth (09:32.333)

In terms of stuff that we’ve already shipped or started using, we have a lot of internal tooling that I think is really exciting and that I’m excited about. Internal tooling doesn’t sound exciting, does it?

Pete Pachal (09:43.534)

Believe me, I’m excited about it. My reader my listeners are excited about it. We’d love to hear about the internal tool bit.

Taneth (09:49.32)

Good. Well, firstly, in investigations, I think that’s the very obvious place that lots of people have started. It’s allowing us to parse and pattern match within kind of large swathes of information in a way that maybe humans could do, maybe in some places they couldn’t. Similarly, helping us to pinpoint information in large documents or large pieces of information. Again, humans…

could do that, it would take us a lot, lot longer without this technology. And then similarly, we have built within the newsroom, a proprietary tool that we call Orca. That is a tool that turns messy audio into structured data so that we can do lots more with audio files. for example, we took

over 2200 hours of podcasts and had Orca listen to them and help us search around information in order to write a report on how the MAGA base and particularly the podcasting coming from that were reacting to the Epstein files and how that changed their attitude towards the government. That is something that we could have done without this tool but it would have taken

a really long time. was, as I say, huge, huge amounts of audio. And so again, this is something that we just simply could not have turned around in that time without this technology. And so although maybe not net new, I think it’s creating net new outputs in terms of speed and scale of our storytelling, certainly. And then away from investigations within our news wires, our news wires audience is slightly different to the general audience, of course.

and so they have different needs. One thing that we’ve done that I’m really excited about is a new feature called company talks. and that is AI generated reports based on company announcements. So we will take a company press release. We will allow an AI generated report to be created and then an editor will check it. That is net new coverage that we before didn’t offer to our newswires audience. So it’s additive to the experience and similar.

Pete Pachal (12:13.708)

And to be to be clear on that, I just want to clarify that. So it’s like it’s not just rewriting the release, I assume. It’s like it’s you’re bringing context in the journal’s history of report whatever I you tell me, like it’s bringing context to to it, correct?

Taneth (12:27.315)

We’re telling readers what it means. But also similarly, because it’s a news-wise audience, actually often they just want a well-written understanding of what that report says. Exactly. Yeah. We’ve also been able to offer our news wires in different languages using AI translation. So we now have Chinese, Japanese, Korean, French, German. Again, it’s a kind of net new offering that we can take out to clients.

Pete Pachal (12:34.732)

I see. In an expected sort of templated way. I get it. Yep.

Pete Pachal (12:55.01)

Nice. Go on.

Taneth (12:56.487)

Go on. And then one that we haven’t yet shipped, I think is exciting. So we should talk about it is in the new experience kind of realm. So we’re working on a product right now called Backstory, which will live in our app and it will allow readers to on a given article, understand the context and the background to that story. And one reason I’m really excited about this feature is that it’s really come from a need both

Pete Pachal (13:02.616)

Cool.

Taneth (13:26.277)

internally and of our audiences. So I’m sure you’ve experienced this, that when writing on a long running storyline or topic, you have to put in the B matter, you have to put in the background because it could be that a reader’s coming fresh to that story. Sometimes it weighs our stories down and we want to kind of trim them or get to the new stuff more quickly, but our readers have an expectation that we catch them up if they don’t know.

what’s already happened. The backstory will allow us to offer that to the readers that need it, but for the readers that don’t, get them more quickly into the crux of the new information. I’m really excited about that because I think it takes a problem that we have had for a long time as an industry, and it really kind of almost revolutionizes how we might tell those stories.

Pete Pachal (14:18.03)

Yeah, I think most reporters sort of default to somewhat and again, this is no fault of their own. They’re great on their beats, but they’ll default to sort of speaking to people who are sort of keeping up with everything they’re read writing. And as a as a newsroom leader, I and and sort of a long t you I I had to sort of beat that out of myself as I was doing. And so now I find I kind of do the opposite. But to your point, those are different readers and they both deserve to be served, right? Like in other words, don’t bog down the people who are keeping up, but also there are

take into account the people that haven’t necessarily been following every development. And if that can burden can shift from the the writer or even the editor and AI can sort of take on some of this interpretability, that that becomes very interesting and potentially powerful. And that’s kind of what I wanted to steer toward because I know you’ve written about this, about adaptive content and sort of the the next

sort of phase of that to me feels like what just generally called liquid content, which is like, you have all this facts and reporting and and you can turn that into whatever, you know, you could turn it into an article, you can turn it into podcast, etc. So, sort of break down your thinking on that, on sort of where that can live, because we’ve been talking about sort of newsroom tools. I’m sort of shifting that a little bit into a reader experience. how do you how do you sort of organize your thoughts around like what

the tool is a tool for the storytellers and what’s a tool for the people interpreting it.

Taneth (15:48.964)

Mm-hmm. So I’m very excited about this. And I have been thinking about this for a long time. And when I first started thinking about what people now refer to as liquid content, I was like, I’m a genius. I’ve cracked it. I fixed journalism. And then I became obsessed with it. And I was talking about it and quickly realized that, of course, I was not the only person to see this technology and have this idea. And that’s because it’s a really real problem, I think. that is that personalization, I think,

Pete Pachal (16:02.988)

Ha ha ha.

Taneth (16:18.195)

We have not cracked personalization as an industry. New generations of readers expect things to be highly personalized, whether they explicitly tell us that, or whether just in their everyday experiences on social media, on shopping channels, on Netflix, they are used to seeing things that they like and that they want to engage with. At the same time, we all lived through

personalisation of social platforms doing not great things for news. You know, those feeds became highly personalised and people ended up in quite concerning, sometimes filter bubbles. And so as an industry, we really didn’t want to exacerbate that problem. And so we have, think, shied away from personalisation. Not that’s a kind of real overgeneralisation. And of course, lots of news publishers are doing

really cool things with personalization. But I think as an industry, you know, we’re not offering yet highly personalized experiences. And I think that’s partly because we’ve only looked at personalization through the lens of topic. And we’re saying, this is a really important story, and we don’t want to hide it from people. And actually, it’s the same from a reader perspective. For us at the journal, readers tell us that they want a curated experience, they want us to tell them

what we are seeing as the most important stories that day. so personalization of topic becomes a little sticky for all of those reasons. I think this technology has allowed us all to think about personalization in a slightly different way. And that’s in personalization of format of how a reader might consume something. And that could look like lots of different things. It could look like, me this story, but give it to me.

in audio or in video you know I’m on my commute and it’s 13 minutes long give me a 13 minute audio version of this story it could be that we see that Pete only ever watches videos on the journal app give it to him in a video version I think that’s the first step I think we take it further by personalizing the actual story itself so not just format but

Taneth (18:38.373)

If we see that I engage better with stories that are led with a case study because I’m an empath and I need to see how it affects humans, then what if our building blocks of that story could be rearranged in such a way that we give me that version? And if the story is about a company that I invest in, so I actually just want the numbers really quickly up top, let’s give that to me instead. And so then your information becomes kind of

building blocks that could be arranged on the fly for the right person in a way that is the right way to consume for them.

Pete Pachal (19:17.218)

Hmm. Yeah, it seems like as you were speaking about that there, that the way the the way I am understanding how memory is working in these models, you know, they sort of build up this file over time. and I know OpenAI is doing sort of even more advanced things, but it does feel like this is the media equivalent of that, that it’s like it’s it’s sort of taking that idea of memory. I was like, and it so it’s not even anything I I I specify.

in the app, it just sort of understands, this is from my behavior. I’m doing this. So starts to build up this memory. And sure, there might be some setting I just turn on at the very beginning of this, but over time the experience of coming to the journal would just evolve to just match my needs and not at both sort of the app level, the story level. is that sort of a fairly accurate picture of kind of what you’re you’re thinking about?

Taneth (20:11.405)

Yeah, and I do think audiences are going to expect it. They are going to come to expect it. Especially as, you know, time is our biggest competitor and younger generations are turning away from the news. And one of the reasons we know from studies that one of the reasons that they’re turning away is that they don’t feel that the news is relevant to them. And I think this goes a long way into taking

the news of the day and giving it to people in a relevant way that they feel will impact their lives.

Pete Pachal (20:46.476)

Right. I know you didn’t mean time the publication there, but I was just to clarify for the listeners, I was like, Wait, time is? No.

Taneth (20:51.315)

No, time the concept, I’m sorry.

Pete Pachal (20:58.252)

Yeah. Yeah, no, I get it. I get it. so interesting. So we’re talking about liquid content adapting this stuff. It’s great. So what what changes about the journalist’s job then as a reporter or editor? Anything? do they have to sort of get ahead of some of these formats? and if it sort of becomes this thing where the final content’s malleable, I think sometimes reporters fear that, well, I’m just like a fact.

Taneth (21:01.171)

you

Pete Pachal (21:24.588)

I’m putting facts in a in a robot or an engine and it’s just creating things with it. tell me tell me what your vision is on the sort of news production side.

Taneth (21:34.558)

think firstly, there will always be a place for a well-written narrative yarn. know, we have seen books are still here. People have predicted the end of books for a long time and they’re still here because we want them. And I think similarly, kind of long, well-reported narrative pieces of journalism.

will survive. That’s the first thing that I should say. But there are some forms of journalism that are there to deliver new pieces of information. And I think that is where this kind of technology will play. And so I do see a future that a really brilliant reporter will spend the majority of their time going out and getting facts and filing them in those building blocks.

the new piece of information, the quotes, the characters, the rights of reply, the images, all of these kinds of pieces of metadata that we can use technology to build many, different end results. And look, frankly, I think a lot of reporters will be pleased to hear that. Lots of reporters tell me that the best part of their job is going out and finding the facts. And so I think reporting is going to become

a very premium requirement, you know, like I think it’s going to be more important than ever. And but that’s not to say that there won’t be it’s kind of a spectrum, you know, that everything’s everything’s a spectrum, there will be I really do believe then a premium on the also the really in depth well written kinds of journalism to

Pete Pachal (23:25.708)

Nice. And you you mentioned earlier, you know, there’s some reporters who sit on the committee you talked about, and they’re all obviously probably enthusiasts. I’m sure within the newsroom there’s a spectrum there too of people who are all in on this. It’s great. They see it as a very great tool, and some folks that might need some some coaxing. And can you give me a sense of kind of what the transition’s been like? I mean, transition, I guess, in terms of like the AI era, cause some come to think of it, but like

How have you been able to get catch up some of those folks who might be a little skeptical of like this whole AI this all AI thing and how it affects their job and their industry?

Taneth (24:07.513)

Mm-hmm. I think that it’s actually been an interesting challenge because it has come at us with such a speed, this technology, that everyone, you could take a room of people and everyone would have a slightly different level of experience or understanding. So other things you can kind of launch training sessions and get everyone together and talk about it. This is, it has been quite a unique challenge.

And one way that we’ve tackled it in the newsroom is by running kind of brown bag, know, lunch and lunch, come along and see how other people are using this technology in their work. They’ve been really successful sessions because I think AI can sometimes feel like we’re doing a lot of like kind of broad talking about it. And then you kind of get to it and you’re like, well, I kind of had that myself. I was like, yeah, AI, great. And then I sat down and I was like, hmm.

So what should I do? And it was kind of only when I started to practically get my hands dirty that I was like, starting to see more opportunities within my kind of personal sphere and workflow. And so those brown bags have been a great learning tool for the newsroom because they’ve seen how their colleagues are actually practically using things and that sparked ideas. It also means that we can have good like no dumb questions sessions that people can.

really at any level come and say help me. Again, all credit for this must go to my head of data and AI Tess who’s run these sessions. And the next thing that she’s running during the summer are a series of vibe coding sessions. So again, getting people in, it’s this thing that they all kind of vaguely know about and talk about, but then practically don’t know where to start. And so again, it’s kind of putting the technology into everyone’s hands and saying,

Okay, come on, let’s talk about your ideas and where you might use it.

Pete Pachal (26:05.516)

Nice. Yeah, these vibe coding I think is obviously very powerful, but it also has this thing that it it if it’s not managed well, it feels like you know, it just becomes this wild west and people kind of doing duplicative stuff sometimes. I’m not sure what stage you’re at or whatever, what you’re thinking about, if there’s a long term vision for that. But I’m curious if you’re thinking about like how you would transition from someone doing something interesting and very cool with vibe coding and

putting that into like some actual product if there’s enough innovation there and enough interest.

Taneth (26:40.023)

Mm We’ve seen a few examples of internal tooling. So Brian, who’s a member of our social media team, vibe coded a solution for creating social posts, which I just totally oversimplified. Poor Brian. And he kind of brought it to the AI working group who took a look at it said, Yeah, pretty cool. And then we were able to

Pete Pachal (26:56.195)

Mm-hmm.

Taneth (27:07.703)

ship it and offer it to the whole social media team. We’re lucky that we have good allies in product who can help us kind of jump in and make these things a reality. But I think we’re still kind of starting with that. But again, I must give all credit to the working group. They are the front line of this, you know, the ideas come in, they have a look, they sometimes augment them. And they’re really very aware of everything that’s happening in the newsroom. To go back to your point of kind of

launching similar things and everyone kind of having similar problems. think again, we don’t want to innovation. You want people to get their hands dirty and try things, but then equally you don’t want 10 different tools out in the world doing the same thing. So I think right now our work is at such a scale that it can go through TAS and the working group. And so that really helps us. What that looks like.

when we have a thousand people vibe coding things, TBC.

Pete Pachal (28:13.036)

Nice. So I know you guys did some stuff with chat bots and chat experiences. I had Joanna Stern on back when you had first launched the Joanna bot a bit ago. I she’s no longer with the journal, but the I was curious what you learned from that specifically and and what your take is on like chat bots in general vis a vis media sites. Do you have a do you have any thoughts there?

Taneth (28:36.369)

Yeah, I’m reluctant to launch a chatbot, capital A, capital C. I think not just with AI, we’ve seen for many, many years that if you put too much expectation onto a reader or a user, they become overwhelmed and they don’t know where to start. I mean, of course they do. If you said to someone, talk to this generic chatbot about the Wall Street Journal.

Pete Pachal (28:40.386)

Hmm.

Taneth (29:02.503)

you know, where would they start? And it’s the same with when we make, you know, user interaction, but when we build user interaction into our journalism, we have to prompt, we have to help them. It’s, you know, that’s on us. And so I think the Joanna Bot works so well because it was a specific kind of niche thing. Readers were prompted, they were helped along. We also did the same with Lars, which was our tax bot, which it was utility.

offered readers a service and said this is what we will give you. You can ask us your very specific questions when it comes to tax for example. I think that’s what you need to do. You need to help someone into the experience because if you offer them look at anything wherever you want, whenever you want, however you might like to do it, I think we’d be disappointed in the engagement rate.

Pete Pachal (29:32.334)

Mm-hmm.

Pete Pachal (29:55.021)

Yeah, I I totally I’m seeing that too. In other words, like the more successful ventures into this idea of a chat experience are always sort of super targeted, whether it’s something like on iPhones or taxes or in other places I’ve seen election stuff. you know, we’ll see how it evolves. But I also think this transitions nicely into the whole idea of discovery because I think that also like it’s like what do you expect from a media

specific experience on something like the journal versus like your broad chat GPT perplexity Google AI overview experience, right? And so like you know, I sometimes feel like media companies doing chatbots is like media companies doing Facebook or you know, their own social network or something like that. It’s just it’s I don’t not what we’re looking for. but again like it’s all to me it’s it that transition, okay, well what are they looking for from things off the journal? And how does the journal content then interact with that?

and so obviously more and more people are using AI to to use information. the journal is pretty famously in some some has deals and and lawsuits among the major AI companies. Won’t get into that. But I’m I am just curious on how you in your role think about the the broader public getting good information from these and the journal sort of being a part of that in some

Taneth (31:17.244)

Yeah, it’s interesting, isn’t it? Because there are two like broad routes you could go. There’s one to

kind of go all in on your own product and try to get people directly. And there’s one to make your journalism as easily possible as possible so that people can still encounter it. And I don’t think that’s an AI specific problem. I think it’s been the same. We had Facebook and some articles. We’ve had all of these. These questions have arisen before. I think broadly, we don’t want to disappear.

Pete Pachal (31:29.39)

Mm-hmm.

Taneth (31:54.897)

is a good example. is a slight aside from AI, but TikTok is a great example. I’m not driving traffic from TikTok, but I want us to be on there because I want that generation of users to know that we exist. And it’s a similar thing. So, you know, we are thinking about how we show up in those experiences and making sure that our information is visible. But really what this has doubled down for me is the idea of us being a destination in ourselves. I think

in, I don’t know, 10 years time, maybe, maybe I’m overshooting that even. think websites won’t be visited by humans, they’ll be visited by our agents who are coming to collect the information of the day. But our apps will become really important because that will be the human touch point. It will be people coming directly to the journal. And so we need to give them very, very good reasons to have that direct relationship with us to come.

directly to us and not get our information elsewhere. And so that means our information being excellently accessible, yes, and in a wonderful, pleasing experience. But it also means offering other things. Community, you know, how might you come and talk to other readers about our journalism? Live events, coming and seeing us in person, Chakra, having actually a human relationship with us. It could be other…

features that you can only get at the journal. We need to think really, really carefully about fostering that direct relationship with readers at the same time as allowing our information, I think, to appear in a well attributed way in other experiences.

Pete Pachal (33:38.467)

Yeah, and and Fr I take it then you practice some amount of you know generative engine optimization in what you’re doing to make sure it’s machine readable and that you know I know there’s that’s there’s a difference between that and like just blocking unauthorized crawling, right? But if it is authorized crawling, you know, like you want to make it as as as machine friendly as possible. Is that fair to say that that’s the approach?

Taneth (34:02.076)

Yeah, yeah, but I mean, and I say it kind of like, what is that optimization? You know, like, we’re all kind of talking about it as if it’s a thing. And I’m like, is it a thing? I don’t know. I mean, I’m seeing a lot of promises that you can optimize in different ways. And I think there are things that we can do to make sure that our, you know, our journalists information is up to date and that we are very, you know, our metadata is good, but equally.

I don’t think there are like tricks yet. Maybe there will be, but I think, and actually I think the same about SEO too, that I think build a good website in a way that the internet works and do really good journalism and good things will follow. mean, you sure, I’m sure that’s a purist view, I, I, I’m skeptical that you can really kind of game this.

Pete Pachal (34:34.904)

Right.

Pete Pachal (34:53.794)

No, no.

Pete Pachal (34:58.552)

Well, I also think AI is evolving so rapidly that what rules you might sort of conclude at now might might be radically different as they get sort of better at interpretability. That said, I’m happy to do a brown dagger anytime you want on what I’ve what I’ve put together on this. I I think about it quite a bit, GEO and that sort of thing. so I I I’ve again I’ve read some of what you’ve written. I really liked what you’ve said before about pulling back from traffic chasing.

And you know, having sort of different KPIs than sort of the traditional ones that I think, you know, everyone’s kind of moving away from because obviously search and social just aren’t aren’t really are are being reduced in terms of their discoverability for content. Can you talk a little bit about like how you’re thinking about success with regard to both, you know, the story level and also just sort of just broadly as the journal as a as an enterprise?

Taneth (35:57.341)

I actually don’t think it has drastically changed for us. We arrived three years ago and quickly articulated to the newsroom that we wanted to become a truly audience first publication. What does that mean? It sounds very straightforward. It sounds simple almost. But if you really stop and think about it, it’s…

quite revolutionary, it’s stopping at every decision and saying, what does the audience need from this? Because often the audience need is not what our instinct says journalists might be.

So the newsroom has really successfully kind of come aboard with this ethos. It means that our journalism is, we have something called the digital pause. We ask everyone to pause at the start of any process to ask who is the audience for this? What do they want? What are their needs? Therefore, what should we do? And I think that’s, and I don’t just think I can see,

in our engagement rates that it’s reactive readers are reacting well to that. They’re spending more time with us. They’re finding our journalism more readily. And they’re canceling their subscriptions at lower rates, which is, I suppose the ultimate goal is our net number of subscribers and the amount of money that we get from them.

Pete Pachal (37:18.146)

Nice.

Taneth (37:26.74)

So in the newsroom, I think we will continue on that path regardless of AI, of creating journalism that people want to read and then once they start reading it, they stick around. I use read and I shouldn’t use read. also, they may be watching it, they may be listening to it, they may be experiencing it in other ways. And so I think…

For me, the hallmark of a good strategy is that you’re not changing it all the time. And so really the goals that we set out three years ago are still our goals now. We’re using different tactics. But ultimately it is to grow our audience and retain them.

Pete Pachal (38:06.636)

Nice. So as we wrap up here, I try to pin down the people I talk to about things they are both worried about and hopeful about for AI and how it’s changing the media ecosystem. So I’d love it if you could give me one of each. What are you what are you what are you kind of losing sleep over with regard to AI? And then what is like the thing that was wow, this would be amazing if if it were to come to fruition.

Taneth (38:34.93)

I’m worried about facts and their attribution. I’m worried about facts or misinformation taking on a life of their own if people are no longer going directly to the source. I am very excited about and supportive of…

AI technology, but I, like other people, can’t help but notice the confidence with which it gives me information. And I fear that if, if we don’t work really hard on media literacy and people questioning facts when they’re not coming from a trustworthy source, then I fear, I fear misinformation.

and similar kind of filter bubbles. I think that’s my kind of existential bit.

Pete Pachal (39:33.55)

No, it’s good. Hallucinations are I do feel like they’re kind of inherent to the technology, in my experience anyway. Every time there’s a good a new model, I’ll do some kind of rudimentary query and it pretty quickly I get some very confident, incorrect answer. And it’s like, okay. so yeah, I think that’s a fair worry.

Taneth (39:52.338)

And I mean, of course, you know, the internet isn’t built for LLMs, it’s built for Google, you know? And so, of course, it’s going to get things wrong. We’re kind of not helping it right now. I do think it will improve as the output is only as good as the input. I think the input right now, as in the entire World Wide Web, is like not structured for this. So of course, that’s going to happen. I do think it will improve. And I do think as we all learn,

Pete Pachal (40:05.902)

Mm.

Taneth (40:22.024)

good stuff in, good stuff out, we’ll get higher quality answers. But that will be contingent on a lot of education and making sure that everyone kind of has access to the right technology and information, I think. Okay, on brighter note, I’m really hopeful that this technology will allow

Pete Pachal (40:41.144)

Nice. Yeah. What’s the thing you’re hopeful about?

Taneth (40:50.716)

us to deliver information, good quality facts, news and information to more people. Because I hope that more people will want to interact with the kind of news and information that we’re delivering them because we’re doing it in a more effective manner. I spoke earlier about younger generations turning away from news. And I fear that I think it’s

really, I think it’s an emergency that we are creating things that are relevant to generations in a way that they want it. And I really think that AI is going to help us do that in a way that we’ve never been able to do before.

Pete Pachal (41:31.82)

Nice. We’ll leave it there. Tenneth Evans, thanks for coming by and sharing your thoughts.

Taneth (41:35.912)

Thank you so much.

The post The future of journalism is personal: How The Journal is building AI for readers, not robots appeared first on The Media Copilot.

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.

The post The Fable 5 pullback turns AI availability into a planning problem appeared first on The Media Copilot.

This episode is sponsored by: Adobe Acrobat

This week on The Media Copilot, Pete Pachal sits down with veteran AI journalist and former Fortune reporter Sharon Goldman to discuss the growing disconnect between AI’s promise and public perception. As Sharon launches her new Substack, Ground Level AI, she shares why she’s shifting her focus away from model releases and Silicon Valley headlines to examine how AI is impacting communities, businesses, governments, and everyday people.

“To say you’re covering AI today is like boiling the ocean.” — Sharon Goldman

From AI data centers and cybersecurity risks to job displacement fears, media disruption, and public trust, Sharon offers a grounded perspective on where the AI conversation is headed next.

Listen or watch:

• YouTube
• Spotify
• Apple Podcasts

What we cover

 • Why AI companies are struggling to win public trust
• The growing backlash against AI and what’s driving it
• How data centers, infrastructure, and policy are becoming major AI stories
• Whether AI’s impact on jobs is being overstated
• The future of journalism in an AI-powered information ecosystem
• Why independent voices matter more than ever in technology reporting
• How Sharon uses AI as a reporting partner, editor, and research assistant
• The biggest AI stories to watch heading into 2027

If AI is reshaping society, who gets to tell that story? Sharon Goldman believes the most important AI stories are not found in the latest model release or product announcement. They are happening in communities, workplaces, governments, and everyday life, where technology is creating real-world impact.

Sponsor:

The new Adobe productivity agent orchestrates tools and models to generate images, text and rich content like presentations, podcasts and social posts, while also powering conversational PDF editing in Acrobat.

With new PDF Spaces capabilities, users can combine files, links and notes into interactive, shareable spaces for research, collaboration and content creation. VICE News, Kid Cudi and celebrity event planner Mindy Weiss are already using these tools to build trust and deeper engagement with their audiences.

Link: Do that with Acrobat: AI-Powered PDF workspaces | Adobe Acrobat

Why this matters

AI is no longer just a technology story. It is influencing how businesses operate, how information is distributed, how governments make decisions, and how communities adapt to rapid change. Understanding AI’s real-world impact is becoming just as important as understanding the technology itself. As adoption accelerates and public skepticism grows, the conversation is shifting from what AI can do to how it affects people, jobs, infrastructure, and society as a whole.

About the 👤 Guest  

Sharon Goldman on LinkedIn 

Ground Level AI  

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

TMC- TRANSCRIPT SHARON GOLDMAN

Pete Pachal (00:19.51)

Hi, welcome to the Media Copilot. It’s a podcast about how AI is changing media, news, and communication. I’m your host, Pete Paschel. I covered tech for a long time as a journalist, and now I have deep conversations with media people, with builders, and with creators who are all answering the question how will we get information in the future? And how will that change the jobs and the industries whose business is information, especially media? My guest today is Sharon Goldman.

Sharon has been one of the most thoughtful reporters covering AI, most recently at Fortune, where her work has gone well beyond product launches and model rankings. She’s covered AI as a business story, an infrastructure story, a labor story, a security story, a policy story, and increasingly as a story about power. Who controls the systems, who pays for them, and who has to live with the consequences. Now Sharon’s moving into a new chapter. She’s leaving Fortune to launch ground level AI.

A publication focused on AI as it meets the real world. Infrastructure, geopolitics, societal issues like jobs and the environment, all the messy parts that don’t fit neatly into a press release. So today we’re going to talk about what she’s learned from covering AI up close, where what she thinks the industry still gets wrong, how the public’s responding to AI, and what all this means for media, journalism, and our information ecosystem before we get into that though, please just take a second to rate or review the show if you can. If you’re listening on Apple or Spotify, please leave a five-star review. Or if you can, maybe a nice comment. And if you’re watching on YouTube, please like the video and subscribe to the channel if you don’t mind. Those things really do help people find the show. All right, housekeeping over. Sharon, welcome to the Media Copilot.

Sharon Goldman (02:10.748)

thank you so much, Pete. Thanks for having me.

Pete Pachal (02:14.237)

Awesome. So before we get into all the things that I was promising there, I’d love to hear just a little bit more about you, your background. I know you’ve been covering AT A AI for quite a while, not just at Fortune, but other publications. Tell us a little bit about your background, how you got into journalism and how you got into AI reporting.

Sharon Goldman (02:30.584)

Well, as far as getting into AI reporting, that started in April 2022. So a little bit over four years ago, I was hired at a publication called Venture Beat. And the week that I started, the person covering AI left to go to TechCrunch. And I raised my hand and I said, Well, I’ll cover AI full-time as my daily beat. And that was six months before ChatGPT came out, and I’ve been on a roller coaster ride ever since.

Pete Pachal (02:55.533)

Well timed.

Sharon Goldman (02:58.776)

As far as my journalism career, I’ve been in the business for over 25 years. I started out in B2B journalism in subjects completely unrelated to technology. But about 15 years ago, I did start covering technology from the standpoint of marketing tech and sales tech, when Salesforce first came into the picture, when SEO was new, when social media was new. So I feel like I’ve

Pete Pachal (03:26.389)

Well those are the days.

Sharon Goldman (03:27.788)

Those were the days and I feel like I’ve constantly been on this trajectory of of being at sort of on the front lines of when some of these technologies got started and that always floats my boat. So it’s been a really fun and exciting journey that I’m thrilled to continue.

Pete Pachal (03:47.266)

Yeah, it’s pretty wild to know s note how much all of that stuff has changed as you were sort of describing all those areas. And AI, of course, is like always changing. Like I feel like, you know, everything changes, but like, holy cow, is it so different from when you first started it, even in 2022? So, you know, tell us about like what you’re doing now, because you’re leaving fortune to start this new publication, ground level AI. What made you decide like this was the moment here in 2026 to do that?

Sharon Goldman (04:13.196)

I feel like there were a few things converging for me. For one thing, it’s kind of in my DNA to be a bit of a builder. I’ve always had an entrepreneurial bent. Before I started covering AI as a full-time daily beat for venture beat and then fortune, I actually was a freelancer for over 10 years. and I was very successful doing it. I really loved it. I loved that kind of autonomy and sort of building a business on my own.

So I feel like it’s a little bit going back to my natural habitat in that regard. I also feel like the timing is really right. This is such a momentous societal shift that deserves all kinds of coverage. You know, I feel like a lot of coverage right now, you know, really hones in on the biggest companies, the biggest models, you know, the the the gossip, the drama.

But you know, I’m really interested in digging into it’s such a broad beat at this point. Like to say you’re covering AI is like boiling the ocean. So I feel that there’s a gap, you know, in being able to narrow that down to some of the things I’m most interested in. And, you know, after, you know, over two decades in the industry, I feel like I have a voice, I have a point of view that I’d really like to.

get across. And I finally I feel that there needs to be more independent female tech voices out there in the independent journalism ecosystem. And you know, I’m definitely ready to jump in on that.

Pete Pachal (05:48.77)

Nice. So you described, I think, ground level AI as like it’s AI meets the real world, right? And what what does that mean in practice? It seems like that’s a good reflection, I think, of like a lot of your coverage of fortune, which talks about like, you know, these ground level consequences of data centers, the skepticism around it, et cetera. are are you gonna continue in that vein? does it change at all? Do you add to it? How is how is ground level AI gonna cover AI in the real world.

Sharon Goldman (06:19.862)

Yeah, so AI in the real world can can seem a bit vague. To me, it’s really about this societal shift that we’re seeing right now. And that, you know, is physically when it comes to data centers, and we’re seeing, you know, a tremendous build-out, but also a tremendous backlash. And when those two things converge, you know, there’s a lot going on. I also see it as far as you know, just policy, how governments and organizations are going to handle this shift going forward when it comes to issues like labor, when it comes to issues like safety. You know, that’s everywhere right now. With a big election coming up in November, I’m really eager to dig into that. I’m also keenly aware of security issues that are every single enterprise is needing to invest in this right now with anthropics mythos.

You know, kind of changing the game and giving enterprise companies a wake up call about what they need to do in their organizations to make their systems secure. I think that’s the real world too. That’s ground level change and that’s what I really want to dig in into.

Pete Pachal (07:36.344)

Yeah, and also feel like you mentioned the election there in terms of AI meeting the real world. You know, I feel like that’s actually happening in a sort of mainstream consciousness way, right, in this moment too. And I’d love to to get your thoughts on, you know, the public’s relationship with AI, which, you know, certainly in recent months has seemingly taken a turn for the negative. there’s been a lot of shall we say skepticism about AI that’s gone mainstream. You know, there’s the the infamous graduation speech I guess trend that that where people would mention AI and get booed. I’d love to get your sort of initial thoughts on that and let’s let’s sort of double click on a few.

Sharon Goldman (08:18.86)

I feel like I’ve been beating this drum for quite a while. A few years ago I was starting to write essays and and analysis pieces where I the communications piece has got to get better, I think, from the AI company standpoint, if they want the public to get on board. You know, you can’t, in my opinion, say in the media that you know, half of all white collar jobs are gonna go away by this time and then expect everyone to be excited about the photos and images and and writing that they’re doing using their tools. So I feel like there’s a tremendous disconnect. you know, and then where is the public getting their information from? You know, they’re certainly getting news from social media and there’s a tremendous backlash on social media to AI, whether it’s from the data center standpoint, whether it’s job loss. Also just I feel like there’s a bit of a disconnect, partly because it’s how the technology is. It’s these use cases now, whether it’s writing or coding, these are not the world-changing use cases that people would like to hear to think that they have to suffer for it.

If AI really was, for example, curing cancer, well, that would be a different story. But if it’s simply making your enterprise workflows better or creating an app, people commonsensically are not going to see that as potentially worth it. But of course, the other side of that is that AI is an exciting technology with a tremendous amount of potential for humanity.

But I don’t think that’s being communicated very well. And from a politics standpoint, that’s going to be big in November.

Pete Pachal (10:16.257)

Yeah, it’s really interesting like the communications aspect of it that you touched on there because it here’s a little theory I have. I’d love to hear your thoughts on it, in that a lot of I obviously cover media and AI all the time, and there’s a lot made of how the media is now reacting to AI with the knowledge of what happened in search and social and that it all eat up their business model. So there’s a very sort of defensive view it has generally. But there’s a flip side to that.

Right. There’s and this is I feel like this is what may have sort of guided the comp, like whether it’s a strategy or whatever, like the I because here you here’s again the bear with me here. But like the jobs stuff, let’s just take that. They were they were crowing about how AI was going to kill all the jobs, like people like Dario Amade and everybody, before there was data, you know, and and so far to my knowledge, again, you correct me if I’m wrong, because you’re on top of this more than me.

I don’t think there is data yet that they’re that AI is like massive job loss. You know, there’s the anecdotal stuff, of course, but like the it’s just not there. So it’s a weird thing that they decided to come out ahead of it as sort of a comms tactic. And I feel like their lesson from that era is like, let’s talk about the negatives before they become an issue so that we’re not, you know, pilloried for it later or something. Was that

Do you think is that a theory? Like what do you think? Like is I to me it confuses me why they’re the worst pitchmen for their own tech because they they’re like kill saying it’s gonna kill everybody and before it there’s even any data to support that.

Sharon Goldman (11:48.706)

That is my theory. I I remember a few years ago, Sam Altman saying something like, you know, we’re we’re putting this technology out so people can get used to it, so they can kind of understand and in and embrace and experience what’s going to happen before before it goes further, before it gets even more advanced. And in a way, I felt like the job loss idea is sort of jumping ahead and saying, like, you need to beware, you need to reskill, you need to think about.

This, but again, before really any data has come forth. Also, they’re really thinking about within their own fields. I mean, when it comes to tech, and you know, if you’re a software developer, yes, there are job losses, there are layoffs in tech companies. This is happening to some extent, but that doesn’t broaden out necessarily to every field every position. And it also doesn’t take into consideration the potential for job creation, which, for example, Aaron Levy at Box has really been talking about beating that drum on social media lately. that new jobs will be created, that agents are still going to need humans to to manage them, to, you know, and and jobs that haven’t even been thought of yet. That’s what I wonder about.

You know, my husband is in cybersecurity, for example. Well, cybersecurity wasn’t a thing until, you know, twenty years ago, twenty-five years ago. There are there are many jobs like that. there were no social media influencers 15 years ago. So who knows what’s going to come. But I do think that you know, I guess that frontier labs like anthropic and open AI kind of with their they’re they’re also coming with their own baggage of what they talk about as far as AGI, what they believe is going to happen in the future. But again, the timing of that, is that going to happen in two years? Is that going to happen in 20 years? you know, I I just don’t think that that’s the way. Or maybe they just feel like they didn’t have enough to say yet about the positives. I don’t know.

Pete Pachal (14:03.703)

Right, right. And I feel like like you mentioned like of going back to the public sentiment, like are everyday people experiencing the positives. Yes, of course, if the AI could hear cancer tomorrow, we’d all celebrate. That’d be a huge thing. It hasn’t yet, notably. But the the the idea that like it’s making your life easier, better, etcetera, it’s to to me to my like the thing is here’s the thing. You and I I assume you’re kind of a an adopter and a trier, you know, you use this technology too. And we should get I’d love to hear about like how you’re using it. But I’m seeing good gains because I’m a power user, right? And I’m diving into these things all the time. And there’s co-working apps. And it’s like, this so cool. I think that’s very rare. I think we’re like in the story like the 1% of people and it’s a ways out. So this sort of it is anti AI stance. I feel like it’s hard to pin down on it’s not just jobs. It’s not just environment. I think it’s just this broad like

Sharon Goldman (14:32.994)

Yes.

Sharon Goldman (14:42.87)

Yeah.

Pete Pachal (15:00.917)

what’s so great about it sense that sort of people have about it. And then, yeah.

Sharon Goldman (15:05.26)

Yeah, these are not, these are not the power users. Excuse me. These are not the people necessarily even using it at work. you know, what you you talked a little bit about search and social media. I mean, these were things that really were kind of life-changing at the time, like the idea that you could, you know, follow a friend or message a friend or search for people it, you know, that you knew decades ago that you weren’t able to keep in touch with.

Pete Pachal (15:21.014)

Right.

Sharon Goldman (15:33.218)

That was a real life-changing thing, even if you feel that today there are so many downsides. That was a real game changer. Here with AI, I feel like it’s much more, it’s sort of om it’s always sort of what’s coming, what’s coming down the pike. It’s not necessarily what’s here and what you’re playing with it now. It’s also something that does take some doing as far as understanding how it could work for you.

Power users like you and I, we we all spent a lot of time trying to figure out, well, what is this really good for and what is it not you know, fun but not really useful.

Pete Pachal (16:12.342)

Yeah. What do you think AI companies should do in this front in terms of the massive wave of public s skepticism now? And you know, we can touch on the environmental stuff too, if you like, ’cause I feel like that’s a huge chapter of this. And I know you’ve covered that quite a bit. But what are what are the things they can do about this to maybe turn the tide on the sentiment, but at least between now and maybe the election? Well, not not that that necessarily favors one party or the other, but maybe it does. You tell me.

Sharon Goldman (16:34.712)

Right.

Know. I mean, these these companies now have massive comms teams, you know, that that that just are overwhelming. Hundred I remember when OpenAI just had one comms guy and now there are hundreds and hundreds. I do think, you know, I mean, and maybe they feel like they have been doing some radical transparency, but you know, transparency is key. You know, when I report on AI data centers, for example, it’s one of the things that communities

Pete Pachal (16:43.842)

Yeah.

Pete Pachal (16:50.702)

Mm.

Sharon Goldman (17:06.172)

Are so frustrated by kind of the kind of the secret nature of it that things just appear. There are NDAs and things you can’t talk about, and then suddenly, you know, something is appearing before their city council. you know, I think also just you know, maybe empathizing more and not being so Silicon Valley-centric and assuming that everyone is so excited about the the the deep tech or the…

Pete Pachal (17:21.71)

Mm.

Sharon Goldman (17:35.692)

… use cases that require you know several hundred dollars a month in tokens. I mean that this isn’t you know really speaking to the average person and and also making it clear that you know this is this is our opinion, this is the way we see the world, this is the way we see things playing out, but there are other opinions out there. There are other people saying different things.

Pete Pachal (18:03.982)

Let’s switch gears for a bit and sort of talk about the media for a bit, because that of course is what I what I zero in on a lot. well let’s just talk broadly first, actually, about the information ecosystem and stuff. Like what do you think is the biggest thing in your mind about how AI is changing that? How AI is changing what we’re doing? I I assume, you know, vis-a-vis your new venture is that you’re gonna have some kind of website and and how you’re feeling about that in terms of what AI is gonna do to it and et cetera. Maybe you even have some thoughts on whether to block or not block or what have you. But you first tell me like broadly like how are things changing and then maybe let’s make it a little more personal.

Sharon Goldman (18:43.458)

Well, ground level AI is gonna be on Substacks, so that’s one thing. And I’ve already had a sub yes, that’s still the internet. yeah, I mean, I think that, you know, as we all know, the media ecosystem has changed already almost beyond recognition. I think that you can’t you you just can’t rely on people finding you through search. You can’t rely on on

Pete Pachal (18:46.454)

Okay. Well that’s still the internet.

Sharon Goldman (19:12.576)

Google to save you. I think that the that that we’re all you so many media companies are using AI that we all have to think about our voice. We all have to think about what makes us unique and what we can offer that goes beyond the chatbot.

Pete Pachal (19:33.921)

And like the whole idea of okay, people get your information through a summary now, or at least that’s sort of a discovery window of some sort. and I don’t know like how much, you know, you can tell me about like how fortune sees that and if they’re doing things like GEO or what have you. but how is sort of covering this space sort of as you’ve thought about just even even yeah absent your new adventure, whatever, like I’m reaching an audience now through these this sort of you know disintermediation layer, the summarization layer. And

Sharon Goldman (20:05.688)

I actually remember I remember speaking to someone at a conference, a creator, and the creator was telling me, I think this might have been like three years ago, that this person wanted to be discovered by AI. He wanted Google and OpenAI and anthropic chatbots to find him and scrape him and spit him out because it was gonna help other people find him through GEO, which wasn’t even a term at the time.

Pete Pachal (20:19.758)

Mm.

Sharon Goldman (20:35.244)

And at first I was like, wow, that’s amazing that a creator would say, I want to be scraped. But today I think that is real and true. And I would have to agree with that. I don’t think there’s any point in saying, like, you can’t scrape my work, you can’t output, you know, something related to me and you know, link to my work through through you know, Claude or Chat GPT.

Pete Pachal (20:35.288)

Right.

Sharon Goldman (21:01.9)

I think that’s happening. And I think that ever every single brand out there is working towards it. I’ve noticed in my inbox several companies that this is what they do. They rep, they they work for brands and they help them get discovered. And it’s a new, it’s a new way to discover. And in my own use of AI, even outside of my professional life, I use it for shopping, I use it for research. I use it for all sorts of things. So I want to discover things through.

Claude and ChatGPT and Gemini in the same way that I would be looking for things through Google search.

Pete Pachal (21:37.507)

Totally. And you d it helps it’s very helpful when it can just bring you that information. but of course it needs to get that information from somewhere, it needs to get the context, and it ingests things like, you know, obviously journalism and other content and gives you the answer. A. G. Sulzberger just recently made a speech at a conference and he was saying that, you know, this is essentially theft. This is like if you’re a may a may building a tower, a construction company, but you get all the materials from the the village next to you or something and you just don’t pay for them. you know, again, I I don’t know how you see it, but if the copyright question is a is a is the thing that’s out there. from your reporting, I guess what I what I’m really curious about, and I don’t know if you’ve covered this issue specifically, but from what you know about the AI companies, like how seriously do they take that point of view and concerns about copyright, you know, vis a vis the

I know they have lawsuits and stuff, but like how do how do you what is your sense of like how they even think about this?

Sharon Goldman (22:40.118)

The AI companies believe that what they’ve done or or they, you know, they their their PR and strategies to communicate that their belief is that it’s fair use. You know, whether that was in their early, you know, whether that’s what they thought initially back in the day, or whether it was more like we’re we’re using this for research and it won’t matter in the future. But at this point, you know, I’ve spoken to OpenAI’s lawyers, for example, and their argument is that it is fair use. You know, this is this was you know, vast swaths of of public material. And even if there is some copyrighted material in there, you know, this is about the output, and the output isn’t the same. And you know, that’s that’s their argument. I actually thought that more of these lawsuits.

From the standpoint of the defendants, the author, the the, I’m sorry, the plaintiffs, the people bringing the lawsuits, authors, et cetera. I had a lot of predictions from lawyers saying that this would reach the Supreme Court, that, you know, that that there was a really good argument there. But I do think the AI companies have, you know, in incredible legal representation and the argument around that what they’re doing was fair use.

Has gotten a lot of traction, I think.

Pete Pachal (24:11.278)

D interesting. So do you think it will end up getting the Supreme Court? I guess ultimately, like, how do you think this question will be settled, at least in the United States? I mean, we’ve seen like o in Europe, for example, there’s well, you know, there’s there’s a different stance, shall we say, and there’s probably a little bit more movement to protect content creators. The UK body just re this is a little different issue, but the UK body recently sort of is gonna be forcing Google to separate their AI crawler from their search crawler so that copyright holders can opt out of AI search, which they kind of can’t do right now with respect to Google. kind of an in the weeds thing there, but like the broader question, how do you think this is gonna be resolved?

Sharon Goldman (24:50.744)

I feel a little

Sharon Goldman (24:55.532)

I feel a little bit cynical there. I I kind of feel like already we’re seeing the copyright issue not as front and center as it was a couple of years ago. I feel like a lot of the arguments have been thrown out by judges and there are some left, but I I I think the the the fact that copyright laws were not written for this age is the big the biggest problem. And so potentially there isn’t a way for a court to say that that OpenAI and Anthropic are completely unable to do this. And I do think that these are two massive companies now with incredible investments in their legal teams, and they have also a strong argument. So I guess cynically I kind of feel like they’re winning on the copyright side of things.

Pete Pachal (25:58.223)

Staying with like the information side of things, I know you’ve covered like the content credentials and C2PA and these standards to authenticate or potentially authenticate like what’s an AI image, what isn’t? I’ve looked at this a little bit too. it’s it’s a little depressing how l how little traction it seems to have gotten so far, at least as far as I can tell. What is your what are your thoughts on this? Does this have a chance of helping us?

kind of detect what’s r real, what’s not out there, is which is obviously a big issue as as more AI content gets distributed.

Sharon Goldman (26:38.04)

I also am a little cynical there. I think that it might help if you like in an individual scenario where you needed to find out where something specifically was real or not. But as far as like the vast swath of slop out there and your average person being able to discern that something is real or not, I think the answer is no, even if there is a symbol or whatever, something can get shared very widely before that’s even noticed or you know whether people would even believe those markings is is another issue. I think when when I’ve spoken to some experts in this field one thing I thought was interesting is the idea of doing the opposite rather than proving something is fake proving something is is real you know I f I feel like

It’s hard to say whether any of it will work though, because I feel like there’s there’s such a vast swath of slop now.

Pete Pachal (27:44.502)

Yeah. Th there definitely is. how do you define slot, by the way? Is it just you anything AI generated or is it anything AI generated that’s either worthless or misinformation or what’s what’s your general definition of slot?

Do we need one? I feel like slop has become fake news, you know what I mean? Like it’s like it kinda means what we kinda know what you mean, but kinda it’s there’s not really a good definition for it.

Sharon Goldman (28:09.634)

There isn’t. And I’m not sure all slop is slop. Like, for example, I’ve been thinking a lot about the political videos that have been going out. They’re AI generated. you know, with Trump, for example, or I’m thinking of like Spencer Pratt, the the LA mayoral candidate putting out videos. Like, are those slop? Like they have a purpose. They’re

Pete Pachal (28:21.504)

Right.

Sharon Goldman (28:35.788)

They’re being used for political gain, they’re sending a message. Is that slap in in

Pete Pachal (28:36.013)

Right.

Pete Pachal (28:41.484)

I feel like slop to your point connotes l low value. And while those are clearly AI, they’re valuable to somebody, certainly valuable to the campaigns or they wouldn’t be doing them. and so I don’t I don’t think it does. I don’t think it it meets the bar on slop, even though it’s so obviously

Sharon Goldman (28:54.06)

Right.

Sharon Goldman (29:01.1)

Yeah, I feel like slop is sort of, you know, vast quantities of low value, c clearly rather useless content, whether images or text.

Pete Pachal (29:12.686)

Mm.

Pete Pachal (29:17.442)

So I’d love to talk about, you know, obviously you’ve been reporting on this for a number of years. You have thoughts on just the how to report on this, which is to say, like, what are the biggest lessons you’ve learned? What makes AI reporting challenging? How have you been able to cover it well despite those challenges? Especially as I’m sure you’ve probably seen the number of AI reporters multiply over the past the past few years.

Sharon Goldman (29:43.288)

Well, absolutely. When I first started covering AI in twenty twenty-two, there there was a community of AI reporters, but it’s multiplied exponentially. And in a way, you know, at Fortune, for example, in a way, everyone is an AI reporter because almost everything we’re reporting on, so much of it has from a business standpoint, has an AI angle. I feel like the biggest lesson I’ve learned covering AI is.

How it has the news the news cycle is so fast, so speedy. What was news yesterday might not even be news anymore the same day. And the amount of news that just comes flowing like a tsunami on a daily basis is impossible for one person to cover. So to say you’re an AI reporter today is, you know.

Not the same as the AI reporter from a few years ago who was reporting on a few companies, frontier companies that were mostly research labs that were putting out groundbreaking research, or they were focused on enterprise use cases that were very much under the hood, that were, you know, unsexy. Today that’s completely, you know, the the amount of startups, the amount of funding, the amount of investment, the the idea that it’s propping up the entire US economy and global economy, the idea that it’s become such an issue of geopolitics between the US and China that it could actually be dangerous is you know, that’s a whole other thing. That requires dozens of reporters to cover every angle and every company.

Pete Pachal (31:25.166)

Mm.

Sharon Goldman (31:34.136)

So I feel like the lesson I’ve learned is to take a step back and be like, well, you know, not what’s the news today, you know, whether it be another embargo or another piece of gossip from OpenAI or Anthropic, but what’s most important to cover? What’s the biggest message to respond to today or to communicate? Or what am I most interested in in covering today or this week?

That you know, I have to step back, you know, for mental health purposes as well as what I see happening in the in the landscape, and say, what what’s really important here for people to know? and a lot of times that isn’t the biggest model or the latest release or the the biggest valuation. It’s really about what’s happening underneath, you know, it’s the whether it’s the

Pete Pachal (32:08.994)

Sure.

Pete Pachal (32:17.326)

Yeah.

Pete Pachal (32:30.712)

No.

Sharon Goldman (32:32.652)

the nuts and bolts of the infrastructure or the safety or what’s happening to communities or what policies are being implemented, what people are talking about behind closed doors. Those are all really important.

Pete Pachal (32:47.414)

Yeah. Do you feel like that’s kind of undercovered these days? Like do you use is there too much in the hype cycle, the products, what these things can do, or ’cause I feel like when I was covering tech, there was always like and I was covering it more from the consumer standpoint, but it was like the stuff would come out, it was pretty easy to get get pulled into like, you know, the reality distortion fields, the hype cycles and get speculative. Yeah.

Sharon Goldman (33:08.522)

Exactly. I I feel I just feel like the nature of it is it’s happening so fast. I mean, I think you know, last month maybe Anthropic had had dozens of releases, you know, you can’t possibly be covering each one you know, as as a small tech team of reporters, for example. so you you you kind of have to get outside that hype cycle. And I do think it’s undercover. That’s part of

Pete Pachal (33:21.197)

Hm.

Sharon Goldman (33:37.677)

what is driving my desire to launch ground level AI is to is to turn my focus to that and leave some of the you know the coverage of the latest drama or personnel move or even you know the latest model change from five point five to five point six to someone else.

Pete Pachal (34:01.336)

Right. Yeah. It’s always like some increment. It’s and it seems like it’s a big deal when the whole number changes, but then it kinda isn’t. It’s weird. I feel like AI, the industry just decided they have they’re not even gonna try to fix their naming slash numbering thing.

Sharon Goldman (34:15.008)

No exactly. The for forget fixing the naming conventions. We’ll just carry on.

Pete Pachal (34:19.094)

Yeah. Okay. Yeah. I teach this stuff and I have to constantly like figure it out, you know, like it’s it’s crazy. but I would love to learn a little bit more about how you’re specifically using AI, in your work and whether or not you might be using one of these coworking apps, whether it’s like Claude Code or or Claude Cowork or Codex, and are you know, what do you find most useful about using the technology and then what are like some red lines for you in terms

of why what you would never use it for.

Sharon Goldman (34:50.264)

Sure. I am I would say that I use AI all day, every day, but it’s really as it really is as a coworker. It’s like as my editor, as my colleague. and that sounds I feel like that sounds kind of cliche in a way, but it really is true. Like I’m constantly and I’m I’m pretty much confined my efforts to using

Chatbots. I use Claude, I use Chat GPT, occasionally Gemini, but let’s say mostly Claude and Chat GPT. And sort of all day long, I’m sort of talking to it in a conversational way. If I’m working on a story, I’m doing research, I’m asking questions, I’m sort of getting feedback on thoughts. When I’m actually writing the story, I’m actually writing my story and then

A lot of times I’ll paste in a a sentence or something and I’ll be like, I feel like this could be better. Or I’m I’m there’s a word that I’m missing here and I can’t think of it. what do you or just what do you think of this? What do you think of this from a sentence structure standpoint? or or or how do you think this larger piece, how what do you think of my organization here? Could I

Should I be reorganizing it in some way? And this is just an ongoing conversation all day. that’s really different than using it for my writing or my reporting, which I just don’t do. I just don’t find it useful for that. I feel like it’s far more useful as an editor. You know, it’s and and that’s something that I mean at fortune I’m lucky enough to have amazing editors.

Pete Pachal (36:28.462)

Mm.

Pete Pachal (36:32.302)

Mm.

Sharon Goldman (36:38.668)

But even being able to hand in a story with an improved first draft from a copy editing standpoint is a step up. And previously, Adventure Beat, I didn’t have sort of editors on every one of my pieces. So, you know, it’s really helpful from that standpoint. As far as code Claude Code, Codex, these tools, I really haven’t gotten

used to using it in a regular way. I feel like partially that’s because as a journalist, I haven’t found the right use case for myself. I’m not, I’ve never been someone who has a lot of organizational workflows. You know, I’m pretty I’m pretty flexible and just kind of like as long as it’s in my Google Drive or my inbox, I can search for it. I I really haven’t found a use case that I personally find really useful for coding tools or development tools.

I also have found them to be a little bit hard for someone like me who isn’t naturally inclined to dig into them. I’m not someone who loves tools generally from you know pre-dating AI. So I feel like as they become more user-friendly for the average person, which I still consider myself to be as far as a tech tools user, I feel like maybe there will be some workflows that I find them useful for.

Pete Pachal (37:33.159)

sure.

Sharon Goldman (38:02.476)

But some of the things I use AI for are just so, you know, run of the mill. I mean, nothing has changed my life more than trans transcriptions, AI transcriptions. To be able to do that in real time on my interview saves me so much time. And that’s, you know, a pretty straightforward use case. But and being able to search those transcripts and being able to ask Claude or ChatGPT to find something in one of my transcripts.

Pete Pachal (38:12.942)

Sure. Yeah.

Pete Pachal (38:30.509)

Yeah.

Sharon Goldman (38:30.7)

These are the game changers for me as a reporter.

Pete Pachal (38:34.284)

Nice. I’d be interested in like circling back with you in like six months to a year and see where you’re at with coworking stuff, especially with w running your own operation, ’cause I’ve found them invaluable. Yeah. Anytime.

Sharon Goldman (38:42.604)

Yeah, I actually think that that yeah. Well, and I’d love to pick your brain brain on that. But yeah, I do think like running my own business, that’ll be a new opportunity for me to test certain things and see how they work for me and could I find them useful. so yeah. Well then I’ll have to take one of your courses then.

Pete Pachal (38:56.856)

Well, I think you’ll find them useful. Nice. wow. Hey, awesome. You heard it here, guys. sweet. So like but let’s look forward as we’re looking forward. I’m I’m curious about what you see as maybe the big stories in AI over the next year. We touched on the election a little bit, but that’s certainly gonna be something I feel like this is probably gonna be the AI election, at least maybe the maybe the first one that where it’s really a major issue. But what else what else are you seeing?

Sharon Goldman (39:11.552)

Mm.

Pete Pachal (39:25.774)

coming down the pike. what what do you think are gonna be the big stories? And also like what do you think will continue to be like the undercovered stories that that you think are the most important?

Sharon Goldman (39:36.889)

I think the AI data center boom story is going to continue to rise. Most notably the backlash. I think it’s growing. I think it hasn’t reached its peak. I still think it’s undercovered in some ways, like from some angles. For example, you know, of course I’ve been covering at Fortune, I’ve done a series of stories on different communities that have been affected by.

AI data center builds or proposals in their neighborhoods, in their backyards. But the backlash has become very activist. It’s become across you know, it doesn’t matter whether it’s in your backyard or not. It’s just people are generally opposed. And there’s been a tremendous rise in conspiracy theories, I’ve noticed. massive Facebook groups that are focused on the fact that they feel like

Data centers are surveillance centers, that they’re really about population control, that they, you know, emit radiation that can cause cancer. That, you know, I’ve e I’ve read some really strange things about people who think that they’re built on they’re being these mega AI data centers are being built on farms because they want to take away people’s ability to grow food. I mean, really out there kind of things. That kind of leads into another area that I’m concerned about, which is the potential for, you know, that backlash to turn violent. I do think that that’s a real issue. So that’s, you know, more things that I think could be reported. the build itself, I do find really interesting, like the different ways that data center companies are working to make those data centers more efficient, smaller, less, you know, impacting the environments.

There will continue to be more discoveries and improvements in that area. And I would like to report on that. I think the AI security issue is going to be huge. I’ll be going to the annual Black Hat Security Conference in August again, and DEF CON, which is the biggest annual hacker conference. And I think it’s going to be incredibly, you know, filled with news, which I’d like to cover because I think that’s

Sharon Goldman (42:02.976)

Cybersecurity is going to be a huge issue. and of course, you know, the the big IPOs are gonna be huge, you know, anthropic and open AI and SpaceX, you know, so from a business standpoint, you know, that’s just gonna be filling the headlines for for months to come. yeah.

Pete Pachal (42:05.976)

Totally. Yeah. With this whole mythos model, yeah.

Pete Pachal (42:12.683)

Mm.

Pete Pachal (42:26.85)

Nice. Well, going going a level up from the headlines, like thinking about the trends that all these things are feeding and and creating, looking forward, like what do you see? This is how I sort of wanna wanna at end most of my podcasts, like give me one thing to be concerned about in a broad sort of trend kind of way, and one thing to be hopeful about that from your perspective in in the AI world. Do it in whatever order you like.

Sharon Goldman (42:52.14)

I think that I think the biggest thing to be concerned about is how society is how how c AI is being communicated to society and how people are understanding it. I think the tremendous backlash to AI generally, like we’ve been talking about, is I mean, you can call it a communications issue, you can call it a technology issue, but whatever it is something is not getting through. This is not like previous technology evolutions where people saw some upsides. People saw upsides from the automobile, people saw upsides from the industrial revolution, from computers, from the internet, from social media. I feel like AI is having a harder time for all the reasons that we said. It’s it’s hard to communicate, it’s hard to explain. There’s

It’s so fast moving. There’s so much coming. And yet, is it any of it really life-changing? Plus, there are all these dangers that keep being talked about, as well as job loss. So I’m really concerned about how the general public is dealing with a societal shift that is so all-encompassing and fast and really hard to understand too. You know, these are people who are not tech forward, you know, for someone who’s not a techie.

on the other hand, I feel like that same thing, the idea of AI as this general technology is also what makes it hopeful because I do think that there’s another way to see it. It it it remains to be seen if job loss is really going to occur. I personally feel that there’s also a lot of jobs to be created. I think there’s a lot of areas that will, you know, maybe grow as a contrast to AI, you know.

in in real life experiences, for example. I do think there’s tremendous opportunity for AI to help with many issues in our global world, whether it’s healthcare or the environment. And there are also going to be so many technological improvements in terms of efficiency and you know, not being as impactful to the environment that I I do think there’s a tremendous

Sharon Goldman (45:16.212)

scope that we can look at for hope.

Pete Pachal (45:19.662)

Nice. Cool. We’ll leave it there. Sharon, thanks so much for dropping by. can’t wait to see what you do with ground level AI. And I can’t wait to have you back once once you’ve done some more incredible stories. Appreciate it.

Sharon Goldman (45:33.72)

Thank you so much, Pete. Thanks so much for having me.

The post AI’s reality check: Why Sharon Goldman is looking beyond the hype appeared first on The Media Copilot.

This episode is sponsored by: Adobe Acrobat

What happens when technology companies stop advertising in media and start owning it?

In this episode of The Media Copilot podcast, host Pete Pachal speaks with Jonathan Hunt, VP of Media & Content at HubSpot and Head of The Hustle, about the growing convergence of technology, media, and audience ownership.

As AI transforms how people discover information, search for answers, and build trust online, companies are rethinking their relationship with media. HubSpot has quietly assembled one of the most ambitious media portfolios in business, spanning newsletters, podcasts, YouTube channels, creator partnerships, and acquisitions including The Hustle, Mindstream, Starter Story, and Futurepedia.

Their conversation explores why audience ownership has become increasingly valuable in an AI-driven world, how brands are adapting to changing discovery patterns, and why trusted human voices may be more important than ever as generative AI floods the internet with content.

Listen or watch:

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Jonathan shares how HubSpot transformed from a software company into a media powerhouse, building a portfolio of trusted brands through acquisitions, creator partnerships, newsletters, podcasts, and AI-driven content strategies. He discusses the challenges of maintaining editorial independence while scaling audience growth in an increasingly competitive information ecosystem.

The discussion also dives into Answer Engine Optimization (AEO), AI’s impact on publishing, and the changing economics of audience development. As AI reshapes how people discover and consume information, Pete and Jonathan explore a critical question for the future of media: will audiences place their trust in publishers, creators, platforms, or the technology companies now operating at the intersection of all three?

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Why this matters

The rise of AI is changing how audiences find information. Traditional search is giving way to AI-powered discovery, and companies are increasingly looking beyond advertising to build direct relationships with audiences.

As content becomes easier to produce, trust becomes more valuable. Organizations that own audiences, cultivate expertise, and build authentic relationships may be better positioned to compete in an environment where AI-generated content is abundant but human credibility remains scarce.

The discussion highlights a broader shift taking place across media and marketing: the growing realization that audience ownership may be just as important as product ownership.

What we cover

• Why AI may increase the value of human-created content rather than diminish it

• How HubSpot built a media network through acquisitions, launches, and creator partnerships

• The strategic thinking behind acquiring brands like The Hustle, Mindstream, and Starter Story

• Why audience ownership matters more than ever in a fragmented media environment

• How AI-powered discovery is changing traffic, engagement, and customer acquisition

• What Answer Engine Optimization (AEO) means for publishers and marketers

• Why visitors arriving from ChatGPT and other AI tools often convert at higher rates

• The growing influence of YouTube, LinkedIn, Reddit, and creator-driven media on AI search results

• OpenAI’s acquisition of TBPN and what it could signal about the future of technology-owned media

• How HubSpot balances editorial independence with corporate ownership

• The role of AI in content creation, production workflows, and operational efficiency

• Whether concerns about a “SaaS apocalypse” are reality or industry hype

• Why authentic creator partnerships outperform traditional influencer marketing

About the 👤 Guest  

LinkedIn

HubSpot Author Profile

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.

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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

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Two years ago, if anyone had told me the most AI-hostile demographic in 2026 would turn out to be Gen Z, I would have laughed. The generation that grew up with screens in its hands seemed like the obvious early adopter, ready to use the tools to look more skilled, more productive, and more employable than everyone else.

Instead, May commencement season turned into an open revolt. At the University of Arizona, students booed Google chairman Eric Schmidt for pitching AI’s world-changing potential. Gloria Caulfield, VP of strategic alliances for the investment firm and real estate developer Tavistock, drew the same reaction at the University of Central Florida when she compared the rise of AI with the Industrial Revolution. At Middle Tennessee State University, students shouted down Big Machine Records CEO Scott Borchetta for the offense of saying the word out loud.

The numbers back up the booing. A recent Gallup poll measuring AI adoption and attitudes among Gen Zers found the share who say they’re excited about AI fell from 36% to 22% in a year. The share who say they feel anger toward AI climbed from 22% to 31%. Older cohorts are skeptical too, but a sentiment swing this sharp inside the youngest part of the workforce is something I’ve never seen for a new technology this early in its lifecycle.

That swing matters because the AI industry has a serious PR problem at the worst possible moment. Anti-AI sentiment is hardening as the midterms approach, and politicians are picking up data centers as a wedge issue, supporting efforts to halt or slow the build-out of the facilities that fuel AI with the computing power it needs to function. If capacity can’t keep up with demand, the cost of compute will keep rising, and that will put hard ceilings on what newsrooms, marketing teams, and comms shops can actually do with AI in the year ahead.

The infrastructure squeeze is already here

It’s already happening. Anyone who runs Claude as part of their daily workflow knows the rhythm of the outages, and Anthropic’s own status dashboard tells the story in red over the past 90 days. The Claude Code boom has driven demand through the roof in 2026, and the company is scrambling to keep up. Anthropic signed a deal to buy computing power from Elon Musk’s SpaceX, and at the same time it closed the loophole that let builders run third-party software on top of their Claude subscriptions. Some of those setups were burning thousands of dollars of compute against a $200-a-month Claude Max plan. Now those teams have to use Anthropic’s platform directly or move to pay-as-you-go.

The angry posts in response were predictable, but the more useful read on the change is that it forced builders to reckon with the actual cost of what they were running. The choices are probably familiar to anyone trying to budget AI spend: switch to a cheaper model, possibly an open-source one, rebuild on Anthropic’s own platform, or shut the project down.

This was always going to happen at some point. As demand grows, free-compute workarounds will keep closing. The industry’s argument is that the squeeze would hurt less if compute were cheap and plentiful, which is the case being made for trillion-dollar infrastructure projects like OpenAI’s Stargate. For AI to deliver on its promises, compute has to flow like water. That means more data centers, and more power plants behind them.

Which loops back to why Gen Z is angry in the first place. Environmental concerns are near the top of their list, and AI’s energy footprint has only gotten harder to ignore since I wrote about it months ago. This isn’t a fight confined to politicians and podcasters anymore. At the companies I advise on AI adoption, employee surveys keep surfacing the same worry, and in some cases it’s starting to shape whether teams use AI at all.

Governance is the new AI strategy

You can argue about whether the pollution and water concerns are overblown. The cost question is harder to wave off. The leaders running the most ambitious AI programs are past the era of handing every employee a ChatGPT seat. They want agentic workflows, automated processes, and rapid prototyping through vibe coding, and they may be telling their engineers to get obsessed with “tokenmaxxing.”

It’s unclear so far how data center politics will play out. What leaders can actually control right now is governance. That’s more than running training sessions on which model does what, although that matters. Real governance is the balance between experimentation and direction. People need room to invent their own workflows, and the organization needs a way to make sure the compute it’s paying for is being put to good use. That doesn’t just mean “keeping costs down” it means accepting that the bill will sometimes be high and being confident the outcome will be worth it.

Through my consulting work with media companies and PR agencies, I’ve watched this play out in practice. One agency I worked with piloted a vibe-coding tool. Usage spiked early as employees tested its limits, and several different teams ended up building near-duplicate prototypes. The thing that saved them was high-bandwidth communication. They ran regular workshops and project reviews, learned from their people, and steered the work as they went. They eventually homed in on the use cases that actually delivered, in their case automating media intelligence, and the experiment surfaced something unexpected. The original platform wasn’t the right one. The agency ended up adopting a different tool and sunsetting the one it started with.

That’s one of many examples, and the lesson behind all of them is the same. If AI agents are going to do real work for your team, they need to run compute-heavy jobs. Compute is going to stay expensive for a while. The way to avoid the kind of top-down restrictions that suffocate innovation is for leaders to define what success looks like up front, get their teams trained on the tools and models, and build systems that surface collaboration and catch waste before it compounds.

That is what good governance actually looks like. The political fight over AI and data centers isn’t going anywhere, but the companies leaning into AI can still find a way through. The goal is to work inside the real cost constraints while shielding the people doing the actual work from feeling them.

A version of this column appears in Fast Company.

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