On this episode of The Media Copilot, host Pete Pachal sits down with Michael Ellis, President of Newstex, to explore one of the most important and least understood battlegrounds in artificial intelligence: content licensing.

As AI companies race to build smarter models and answer engines, publishers are grappling with a fundamental question: How should their work be used, and how should they be compensated?

For more than two decades, Newstex has helped publishers distribute licensed content to research platforms, financial information services, and now AI-powered products. Ellis explains why licensing is no longer simply about granting access to content. Instead, it’s about defining purpose, preserving context, and ensuring publishers retain both value and visibility in an AI-driven ecosystem.

The conversation explores why high-quality, licensed content is becoming increasingly valuable as AI systems struggle with misinformation and low-quality “AI slop.” Ellis discusses the growing importance of attribution, provenance, machine-readable metadata, and usage-based compensation models that reward publishers based on how their content is actually used.

Pete and Michael also examine how smaller and independent publishers can compete in a marketplace increasingly dominated by large technology companies, why collective licensing models may offer a path forward, and how platforms like ProRata and AskNews are building new approaches to attribution and publisher compensation.

Finally, Ellis shares both his optimism and his concerns for the future of journalism in the AI era, explaining why the industry must move quickly to build sustainable business models before the economics of publishing are permanently reshaped.

Why this matters

The battle over AI and journalism isn’t just about copyright. It’s about who gets rewarded for creating trusted information. As AI becomes the primary interface for discovering knowledge, publishers face a defining moment. This conversation explores how licensing, attribution, and new business models could determine whether quality journalism thrives or struggles in the age of generative AI.

Key topics

• Why AI content licensing is about purpose, not simply permission
• How Newstex connects publishers with AI platforms and research services
• Why provenance and context matter as much as content itself
• The economics of usage-based compensation for publishers
• How smaller publishers can compete in an AI-first information economy
• The role of metadata, attribution, and machine-readable content
• AI-generated content versus trusted editorial content
• The future of paywalls and premium journalism
• How companies like ProRata and AskNews are changing content licensing
• What publishers must do now to prepare for an AI-mediated future

🔗 About the 👤 Guest

Michael Ellis  –  President, Newstex

💼 LinkedIn: https://www.linkedin.com/in/msellis/ 

🌐 Author Page: https://www.newstex.com/author/michael-ellis 

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?

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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

TRANSCRIPT 

THE MEDIA COPILOT
Host: Pete Pachal
Guest: Michael Ellis

Pete Pachal 

Hi, welcome to The Media Copilot. It’s a podcast about how AI is changing media, news, and communication. My name’s 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 change the jobs and the industries whose business is information, especially media?

One of the loudest fights in media right now is about how AI systems get their information in the first place. Publishers are watching bots scrape their work, while platforms build tools that answer questions directly instead of sending readers to the original sources.

That’s pushed a creative fight into the open: licensing. If AI companies pay for content and follow clear terms, does that create a healthier and more sustainable media economy? Or does it just formalize the same imbalance that traffic and social platforms have already created—one where the biggest publishers and the biggest AI companies benefit, while everyone else gets left out?

My guest this week works right in the middle of that question. Michael Ellis is president of Newstex, a content licensing and syndication company that’s spent more than two decades moving publisher content into research databases, financial platforms, and now AI systems.

Newstex signs non-exclusive deals with publishers, enriches and packages their content, and distributes it to platforms like LexisNexis and Thomson Reuters, along with newer AI-focused partners like ProRata and AskNews.

What makes Ellis worth talking to is that he’s not just selling access. His public writing argues that AI licensing has to define purpose, not just grant permission, and that publishers, especially smaller ones, need collective structures if they’re going to see real revenue from the AI shift.

Pete Pachal (02:29)
He’s also been willing to call out voluntary licensing frameworks as too weak, arguing that they need real enforcement to actually move the needle.

So today we’re going to talk about what Newstex actually does, how AI is reshaping the economics of content licensing, what separates good source data from AI slop, and whether licensing intermediaries like Newstex are solving publisher problems or just adding another layer between the publishers and the AI companies that need their work.

I’m excited to get into all of that.

But a quick note before we get started: If you’re listening on Apple or Spotify, please leave a five-star review and maybe a nice comment if you can. 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, let’s get started.

Michael Ellis, welcome to The Media Copilot.

Michael Ellis (03:21)
Thanks, Pete. Happy to be here.

Pete Pachal (03:24)
Sweet. So you’re in the middle of a very active and sometimes very messy part of the market, basically where publisher economics meets AI. That means, obviously, you’re dealing with everything from copyright to digital rails, to serving content, product design, everything in between. It must be fun.

Michael Ellis (03:47)
It is fun. It’s kept us busy for the last 20 years and is always changing.

Pete Pachal (03:53)
Nice. Well, I want to get into all that and all the things I sort of set up in the intro, but first, I know some of my listeners might not even be familiar with Newstex. So please give me the 411. What does the company actually do, at least today?

Michael Ellis (04:07)
Sure. Well, for publishers, we’re a content syndication service, and we’re specialized in the research market. So this is one area that I think publishers don’t always have their eye on.

You mentioned a couple of our customers there, LexisNexis. You had an interview with Tracy Mabry from Factiva a little while back, for example. They’re not one of our customers, but they could be. Call me, Tracy.

So, you know, we basically are a turnkey solution for publishers that want to have a brand presence in those research markets through those information platforms, and I could say more detail about that.

But we are also there, kind of on the front lines, looking at new destinations for content on all kinds of AI-enabled platforms. So you mentioned ProRata, for example, and AskNews. Those are also customers of ours.

Quick tip: I actually met Rob through your podcast, Rob of AskNews. So I’m just kind of paying—

Pete Pachal (05:05)
Yeah, Rob’s great.

Michael Ellis (05:06)
I’m just kind of paying it forward here. No, it’s wonderful, and I think we have a good kind of symbiotic relationship going there.

In fact, I’ll just take a minute to kind of dive into that because, you know, you mentioned how many layers are we dealing with here in this ecosystem. And I think one thing that I encounter that we’ve spoken about with publishers, one way in which we help the customers is by—

Pete Pachal (05:08)
Yeah, he’s got a great platform. We’ve got to catch up with him.

Pete Pachal (05:23)
Mm-hmm.

Michael Ellis (05:35)
—really bridging the gap between them and the publishing world, which they may not totally understand. You know, some of these guys are totally from the tech world and they don’t really see things from the publisher’s perspective, or they don’t understand licensing. And we provide the infrastructure for that.

Pete Pachal (05:50)
Well, tell me about your customer. Who is your target customer when you say “customer”?

Michael Ellis (05:55)
Target customer? Yeah, yeah. So it’s basically a company that is looking for licensed content curation services.

So publishers are not our customers. We consider them partners. A customer would be like an information platform that needs licensed, curated content, you know, like a Factiva, as I’ve mentioned before, or LexisNexis.

And Rob was someone that, you know, I reached out to after he appeared on your podcast because he said he was looking for licensed content.

So, just to take a step back, in a broad sense, it’s anybody who’s trying to do something with AI and wants to do the right thing and get high-quality, curated, licensed content on their platform and not just be taking scraped content without a lot of information as to provenance.

So Rob is kind of a perfect example because he is one such developer. He’s looking for quality. He also wants to remunerate publishers, and he wants that bridge.

But, you know, his focus is on his users, right? The people that are consuming the information on the platform. So we help manage the relationships with publishers and also look for new sources all the time based on the platform’s desires.

So when someone works with us, when they sign up to be a customer for Newstex, we have a platform where they can go and they can see all of the sources, all of the publishers with whom we have licensed content agreements, and they can pick and choose which ones they want given their use case.

Pete Pachal (07:27)
So, yeah, you’re kind of operating a licensing marketplace for, obviously, a very specific type of customer.

And so I’m curious how you, I guess, bucket the information that is on your platform. So if I’m, say, a publisher—which I am—but I mean if I come to you theoretically as a publisher and it’s like, “Hey, I’d like to be part of your marketplace,” is there a profile of the type of—you said sort of mostly for businesses and they want research. What is your filter there? And then if they pass the filter, how do you then bucket the people you work with?

Michael Ellis (08:05)
I can speak in terms of categories, but I think what I want to get at a little bit here is more the spirit and the nature of the publishers that we like to work with.

I mentioned we’re over 20 years old, so basically Newstex was kind of born in the age of blogging. And this became a really important source of cutting-edge, you might describe sort of insider information, where you have people, some of them journalists, some of them not, just really, really close to an industry that start publishing online, either through a blog, or you may have specialist publishers that are focused on a particular industry.

And it was—how can I describe it? I speak about our content as open-web content, where we have many, many individual sources that are independent from what you might call mainstream. I don’t know if that term really applies anymore, anyhow.

Pete Pachal (08:36)
Mm-hmm.

Pete Pachal (09:00)
Right.

Michael Ellis (09:02)
Let’s just take one space, okay? So the technology space. I mean, we started out—you worked for Mashable.

Pete Pachal (09:06)
Mm-hmm.

Pete Pachal (09:10)
Mm-hmm. Yeah, quite a while. I was the tech editor there.

Michael Ellis (09:02–09:28)
Right. So I wasn’t there at the time, but I believe that our founder ran into Pete Cashmore, the founder of Mashable. It was at a conference, or maybe he sent him an email. I’m not sure.

But, you know, basically we were always in there—

Pete Pachal (09:21)
Sure. Yeah, Pete’s great.

Michael Ellis (09:28)
—getting in touch with people that are trying to do something new in the media space, given the kind of Wild West of the open web, right?

So we work with a lot of what I would call specialist publications that are dedicated to a certain niche. And we have moved from one publication to the next as they’ve kind of evolved over time.

So, like in the tech space, yeah, you had a company like Mashable that was sold to Ziff Davis, I think in 2017, 2018. But in that period in between, you know, we’ve had relationships with tons of other publications that sort of maybe were also on the cutting edge for six months, 12 months at a time, right?

So we went from something like Mashable, you had PCMag, which is an older one from Ziff Davis, onto TechCrunch, onto Boy Genius Report—

Pete Pachal (10:16)
You used to work there too. You might as well just list all the places I used to work.

Michael Ellis (10:26)
They’ve kind of since evolved, right? And now we’re working with the ones that are kind of most current today in the technology space, in emerging technology, in areas like regulatory issues.

So any industry that is kind of facing potential impact from changes in policy or law or geopolitics. That’s another space.

And then the last one is areas around sustainability. So publications covering not only environment, but also energy, supply chain issues—

Pete Pachal (10:26)
Yeah.

Michael Ellis (10:56)
—and a lot of international coverage as well.

And then the last thing I’ll say is we have a relationship with the Institute for Nonprofit News. So you may be familiar with them, but it’s many, many new business models, local journalism or state-based journalism in the U.S., folks like The Texas Tribune or Chalkbeat. And we basically very, very easily kind of help them move into this AI space.

Pete Pachal (11:17)
Okay, sure. Yeah.

Michael Ellis (11:24)
So that they don’t need to take their focus off their core audience and their core mission and just getting people to their website.

Pete Pachal (11:31)
So that’s very interesting. I mean, obviously it’s a good summary of the kinds of publishers you work with and your sort of philosophy with them.

So I want to sort of jump into the AI component of this, and the discovery, distribution, and essentially, I guess, the value prop that Newstex offers.

Because you mentioned open web, which I think is one of the key things here, where it’s like, if you’re a publisher, obviously, like the ones you mentioned, some of the ones I worked for, it’s on the internet. It’s free. Just go, and it’s all ad-supported.

And now, certainly, the internet with AI as this intermediating and mediating force, the open web seems like it is seemingly getting sort of crunched and devalued.

And so in the old days, I guess, you’d kind of say, “Well, I’m just on the internet,” and, yeah, there’s obviously syndication, and it’s not that complicated. And now AI can just kind of parse that with no problems.

But, well, there are problems.

So this is the kind of thing I want to get at. In other words, from a publishing standpoint where you’re just like, “Well, I’m on the internet, I’m a blog, whatever,” give me a little bit more on the value prop of the quality and the sort of direct connection that you provide to your customers, to these sources.

And then I also want to understand how that works vis-à-vis paywalls and sort of other traditional ways of gatekeeping content.

Michael Ellis (13:07)
Sure. So from the publisher’s perspective, it’s really about brand reach, I would say.

Because when you’re in these platforms, one of the nice things actually is it kind of levels the playing field. And that’s kind of what I was going to say about AI in general.

I actually have quite a positive orientation toward AI with respect to open-web content and the long tail because of the way that it can find a really niche article or publication—

Pete Pachal (13:11)
Mm-hmm.

Michael Ellis (13:35)
—and surface it with respect to a particular query.

So, I mean, basically, if you would like to be in these platforms and you don’t necessarily have your own licensing team, maybe you don’t have a big marketing budget, the research market is an important place, but it’s probably not your core audience. Maybe not necessarily.

So Newstex helps to get your brand in front of those people, and it hits them while they’re at work.

You know, they’re using the content for a piece of research in general, and it wasn’t necessarily intended for them. And then what happens is it kind of forms its own little audience within that platform. Someone will favorite it, or it kind of spreads via word of mouth.

And then what we find is, over time, it kind of gains traction on certain specific platforms.

Michael Ellis (14:30)
And, you know, it might be a publication that you wouldn’t expect.

Just to give a quick example, one that we work with is called London Daily News. Okay, there’s another one in London that covers nothing but the taxi industry.

These guys have a subscription-based model, they’ve got some ads on their site, but they want to target these professionals in this work environment, and Newstex can provide that brand presence.

And then over time, it also does generate—

Michael Ellis (15:00)
—revenue for them.

It’s incremental revenue, which probably doesn’t move the needle, to be honest with you, Pete. I mean, publishers don’t work with us specifically for the revenue. It’s more for just the ability to actually have a presence and an influence in an environment where most of these big AI foundation model providers, or the big information research platforms that we serve, they just don’t have the capacity—

Pete Pachal (15:08)
Mm-hmm.

Michael Ellis (15:28)
—to service all of these small publishers that have excellent, excellent content and are needed in order to service all the queries that people are putting into these AI answer engines.

Pete Pachal (15:40)
So I know you’ve written before that AI content licensing is about defining purpose and not just granting access. And I feel like that’s partly what you’re getting at.

Can you explore that a little better? Like, just, you know, what do you mean by that?

Michael Ellis (15:57)
Definitely.

I mean, again, what I like about AI is that you don’t have to always think about keywords all the time, okay?

You can—it’s kind of weird—but actually it allows you to write more like a human in some ways.

And if you stick to your purpose as a brand, you can zero in on the particular community that you’re serving.

Pete Pachal (16:08)
Yeah. And that’s on both sides of the equation, I think.

Michael Ellis (16:30)
And you don’t think about, I don’t know, what are the questions people are asking on LexisNexis or the London Stock Exchange Group, right? You don’t try to game the system. You just stay true to your core.

And as I say, every publisher we work with has their core revenue streams and their business model, and Newstex is strictly complementary.

So we help amplify the impact of the good work that you’re already doing.

Pete Pachal (17:02)
And then you said the revenue is probably incremental for most folks, but what does an exact, I guess, content agreement look like?

And, you know, I guess that would be a picture of what you think a fair content agreement looks like, which zeros in on, I guess, sort of specific clauses around scope, retention, attribution, pricing, obviously.

Basically, what does a fair content agreement actually look like for AI?

Michael Ellis (17:32)
Well, yeah, for AI. For me, it has to be about usage-based compensation.

To me, the market is still developing, and you need some sort of mechanism to discover the right price for a given use case.

Certainly not for small publishers, I don’t think that there’s a way for us to say in advance what is a fair price for X piece of content, because it will depend.

And so every single agreement that we have with our customers, the platforms that we serve the content to, is revenue-share based. We don’t ask for some kind of fixed fee in advance.

By the way, that’s actually kind of why it works for them as well, because they don’t know what content is going to do well until it does well, right?

So we have the same exact relationship with our publishers. You know, they’ve already worked very hard to produce amazing content. We simply want to make it available as many places as possible.

And then when there’s value to be captured, we capture that value and then we share that back with them.

And the relationship is totally non-exclusive, like you said before. So they can work with whomever else they want and syndicate wherever else they want.

Do you want me to get more specific than that?

Pete Pachal (18:55)
Well, no, I’m just a little curious. No, that’s fine.

I’m curious about the growth potential, particularly with AI.

So we’re here, like, you look at the broader stats of AI growth, the use of it, and I think the most reliable thing I’ve sort of read in this context is from the Reuters annual Digital News Report.

And it said something like 10% of searching—and I’m sort of generalizing here—is through AI systems, but those are sort of the most engaged, newer users.

Now you have very specific customers, right? It’s not as broad as what they’re looking at. And so I’m curious how you see growth of this from your highly specialized customer base.

Are we seeing it—because if you look at those stats, it’s like 10%, but we’re all kind of on the assumption it’s going to grow to something much more massive than that.

And are you seeing a similar growth in both the existing customers that you have and their usage? Also, is your TAM increasing, essentially, as AI grows?

I’m sort of trying to give a picture to publishers who partner with you. Like, yeah, it might be incremental today. Could it be—not necessarily business-saving—but is there good growth potential here?

Michael Ellis (20:22)
There is, and it’s hard to predict.

But we’ve had a couple of cases where the publication fits a particular niche. Take, you know, the Ukraine war. We have publishers that we were working with before that were covering Ukraine.

And you can imagine what might happen when a war breaks out, right? I mean, they become much more interesting, right?

And so it’s one of those chicken-and-egg type problems where I think it’s kind of a difficult one for a publisher to strategize around.

And honestly, that’s why Newstex exists. Because we can say, okay, in this complementary space, which is not your focus, we can be there if and when a big win does happen for you.

And, you know, I can’t say that I have a formula for what does well, but we have some basic notions that we look for when we reach out to publishers and ask them to work with us.

Pete Pachal (21:33)
And I was thinking specifically about smaller publishers. I’m not sure how many you would consider small or where that line is in terms of the number of partners you have.

Can you tell me again how many partners? It’s in the thousands, right?

Michael Ellis (21:50)
Yes. So we operate relationships with over a thousand publishers. We have, like, a thousand-plus license agreements in place.

And they may have more than one publication. So at the moment, I think it’s just past 1,700 publications that we represent.

Pete Pachal (22:03)
I see.

Pete Pachal (22:09)
Nice. And would you divide those into small, medium, large? How do you sort of partition that?

Michael Ellis (22:14)
Yeah, I mean, mostly small, Pete.

Many of them—I don’t have the exact stats—but I would say a large percentage of them have probably, certainly, less than 50 people working with them, okay? Just to kind of give you a rough idea.

And then we have a handful of big publishers we work with, like Ziff Davis. We have relationships with Axios, for example.

So, you know, what happens is we started working with them when they were small, and then they’ve kind of grown up to become a big name, right?

But our focus remains on the smaller publishers together, and I think this is kind of one of the core messages—or, I don’t know, we were talking about purpose before—at Newstex, is to bring these smaller publishers into some kind of a model where they can apply their combined strength.

So Newstex helps to do that.

When we come together, the 1,600 publications, they actually produce 1.5 million articles per month. I mean, you know, and then when someone wants to work with us, we say, well, you can have access to all these smaller publications.

And I think that it’s kind of a win-win on both sides.

Pete Pachal (23:28)
Nice.

Pete Pachal (23:42)
Cool. I’m kind of wondering about what you see working, not in terms of subject, but when someone’s a partner with you.

I imagine there would need to be some kind of machine-readable basics on what they need to be doing. And I think this is kind of an emerging category somewhat in AI.

GEO is a thing that seems to be rapidly evolving, basically optimizing your content for generative engines.

As someone who distributes in this sort of arena, are you seeing anything that surprises you in terms of certain types of techniques or anything that lets certain content punch above its weight?

Or is anything emerging in terms of recommendations you would give to publications, particularly smaller publications, that want to get their stuff in front of the right people?

Michael Ellis (24:45)
Yeah. I mean, in our case, I think the big picture is about putting everything in context. Put your story in as much context as possible.

Just a super simple example: These bots, they like to take a story and at least pull out the names and entities that they see mentioned there.

If you can make sure that you are, in some way, making your content machine-readable, where at least these people names, company names, place names are readily available and can be pulled in by a bot, it can do wonders.

So in our particular space, I mentioned before, you know, people working on these research platforms and favoriting a particular publication. They also say, “Give me any publication that mentions the ticker symbol for Microsoft,” right?

Pete Pachal (25:43)
Okay. Right.

Michael Ellis (25:43)
Maybe you’re not a financial publication, right? Maybe you’re not a financial publication, but if your story has something to do with Microsoft, actually you should pull that entity out at least.

Because it might get picked up by a Newstex or by some other bot because someone’s interested in as much information as possible about that particular company.

Pete Pachal (26:07)
I guess it makes sense. It’s like some of these interesting filters. That’s kind of an obvious one, and I think AI might even find some non-obvious ones as these query systems fan out and try to find different things.

But that’s definitely a good one.

So I know you’ve talked about—or Newstex has talked about—not just metadata but provenance matters for AI.

And I want to explain, what do you mean by provenance exactly?

Michael Ellis (26:41)
Again, it comes down to a type of information that helps people understand the context here, right?

So, I mean, provenance—it sounds like a fancy word—but if you go to a website, is there an About page there? Who is behind this thing? Does it say anything about their process?

They don’t necessarily have to be journalists. It’s, of course, better if they are. But what is the editorial process? Who are the people behind it? Where is it coming from? Who is it funded by?

You know, all of those aspects.

And those are some of the things that my team looks at when we look at signing publishers on. We want to know as much context about you as possible.

Because that is—I mean, honestly, I think it’s the key. We need good content, but we actually need good context as well.

Pete Pachal (27:40)
Right. Yeah, I feel like it’s sort of the grown-up, or the AI version at least, of the old trust signals in SEO, with things like bylines and then bios and having those linked.

You didn’t have the same kind of inference systems looking at patterns that AI does. So it’s a sort of different set of signals now that I hope are a little harder to fake or game, right?

You tell me if you think that is true. I mean, there’s certainly gaming in every system, but in terms of this particular idea of signaling to the system that this is trustworthy, well-sourced content, has that become a harder thing to fake, I guess, is the direct question.

Michael Ellis (28:31)
I think so, because, you know, look, if I want to know who these people are, the more data points that I have, I would say that it’s difficult to fake.

Let’s say you wanted to fake Newstex. A 20-year history of all of the pieces of information that point to—we are who we say we are—I think that’s pretty difficult to fake.

And I think, you know, there’s going to be AI tools—there already are—

Pete Pachal (28:46)
Mm-hmm.

Michael Ellis (29:00)
—that help to detect fakers out there and AI slop and so forth.

So I kind of feel pretty optimistic about that one, actually.

Pete Pachal (29:11)
And I’m curious about how you think of—and how Newstex and other systems treat—AI-assisted content and/or even AI-generated content.

I would hope mostly it’s assisted, but anyway, some people call all AI content AI slop. You know, it’s a popular term, obviously.

But there’s obviously more and more AI content out there. And as I’ve written before, not all AI content is slop. But it’s certainly an indicator. It’s the strongest indicator that something is slop, right?

So how do you balance that? How do you think about AI content, even within the publisher circle, the inner circle of sources that you have?

Michael Ellis (29:57)
We ask the publisher, “How do you use AI?”

Is this content completely AI-free? That’s the first level.

Then there’s, you use AI, but you review it before you actually publish it.

And then there’s, we just let it rip and we put the publication up there.

That last category is one that we’re very careful with.

Pete Pachal (29:59)
Mm-hmm.

Michael Ellis (30:23)
But we actually do have some publishers in the financial space, in particular, that have some very high-end—and they’re highly responsible people—systems where they go in and they actually combine various data points in order to produce a story with a specific scope that is kind of at a level of accuracy that’s acceptable enough to some of these financial information systems in particular.

But in general, Pete, we ask the publisher, and then we make this information available to the platforms.

And that’s another piece of context that helps them decide how they want to treat that particular source.

Pete Pachal (31:01)
I’d love to talk a little bit about your partnerships and ProRata. I’ve talked to those guys about some stuff. In fact, we’re working together on an event this summer, full disclosure.

But tell me about how that partnership came about and how their model, which obviously is very centered on attribution and compensation, complements what you’re doing.

Michael Ellis (31:24)
Yeah, I mean, they’re great. It was just within our personal network.

So somebody who had worked with Newstex many years ago was working with ProRata, and they said, “You should really work with these guys because they can help you scale.”

Because, as you probably know, they’re great. They’ve developed so many direct relationships with publishers, but there’s a limit to how much you can do directly.

And so they like to work with us so that we can help expand the source of licensed content that they have on their platform.

They’re a great complement because they’re also trying to add, I think, to the arsenal that publishers have at their disposal to be able to bring some, as they say, some power, I think, to this market in two ways.

I mean, one, I think the way that they use their attribution to say, look, given this output, these publications really deserve X percentage of the credit.

Right? So you have that.

They also, I think, have a really smart way, for those publishers that are open to it, of helping publishers help each other out through their chat interface, right?

Because if you want, you can just have a chat interface for your own content on your site. But if someone asks a question and if the publisher has allowed other content to be surfaced there from other publishers—

Pete Pachal (32:37)
Hmm, yeah.

Michael Ellis (32:50)
—it helps keep that person on that one publisher’s site, right? And vice versa.

So that’s another dynamic where I feel like, again, I guess at the level of mission, we can say we need to come together as publishers in order to balance, I think, the concentration of power that we see on the tech side.

And ProRata is one way in which they’ve actually operationalized that and kind of brought it to life.

Pete Pachal (32:55)
Mm-hmm.

Pete Pachal (33:17)
I mentioned AskNews a couple of times. I’m curious.

I’ve talked to Rob a few times too about a lot of different things, including the atomic unit of journalism and information, which comes up often when you get deep into this stuff.

And I know they think about their grounded synthetic data as preserving traceability, sort of similarly to the attribution, but maybe not with the original wording so much.

I’m curious how you think about that and, you know, vis-à-vis sort of the core rights issue on content.

That’s what it always sort of comes down to, right? Is it the story? Is it the wording? It’s not really the underlying fact.

So, I don’t know, a lot of concepts here, but I’m just curious what you think about it in the context of that customer.

Michael Ellis (34:08)
I just think, Pete, it’s another level at which your content can exist where it can provide value.

I mean, AskNews has got this system in place in order to produce the purest, best possible form of your content for discoverability in a particular context, right?

I mean, they’re focused on high-end enterprise research and people in prediction markets and finance and stuff. And they’re really, really good at helping people avoid bias in their research.

I mean, you couldn’t do that as a publisher on your own.

That kind of middle layer, that sort of synthetic version of your content where it’s just kind of taking out the key pieces, allows someone to find it.

But if they want to get the value from the original article, that full level, that highest level of resolution, is not going to be there.

You know, like on some stock photo sites where you can get a low-res version of the photo, and maybe it’s good enough for your particular purpose.

But if you want the real thing, it’s going to say, well, you should go to this particular location, right?

And I think that the market is going to continue to evolve to the point where, okay—

Pete Pachal (35:17)
Yeah.

Michael Ellis (35:32)
—we know now we want this particular article, or we want more from this source.

Now can we create more value when someone says, “Give it to me. I want to see the full text,” right?

So I think it’s going to evolve to produce more points of value for publishers that they can eventually monetize.

Pete Pachal (35:45)
Yeah.

Pete Pachal (35:52)
Yeah, I feel like you’ve actually sort of awoken something in me with the image analogy, because I do feel like this is the discoverability problem—not quite solved—but one aspect of it.

I’ll tell you what I mean.

What I think those photo sites are incredibly good at now is showing you the free image and showing you the premium image right beside it that’s, like, better.

And you kind of go, “This’ll do, but I really want that one.” And once that happens enough times, okay, I’ll buy a subscription, right?

So similarly with AI information systems, you’re kind of like, well, here’s the summary, but it’s really very surface, and what I really need is the underlying data, reporting, et cetera, to get what I want.

If you have the right audience, which I think is kind of the problem Newstex and other things solve, that will be the thing they do if they see it enough times.

Am I sort of getting that? Is that sort of where you’re going?

Michael Ellis (37:03)
Yes, definitely.

I mean, I think it’s nice because it allows some publishers to freely share their information without feeling like the original text is just out there.

Because historically, something that we still do with some publishers is, you know, they give us some of their content, but maybe they have a paywall for—

Pete Pachal (37:25)
Yeah, that’s what I was just going to ask about. How does that work vis-à-vis a paywall?

Michael Ellis (37:32)
We take whatever a publisher is open to syndicating, and it’s their choice, however they want to do it.

It can work because, you know, these are professionals doing research. They come across your content, they see what’s been freely made available, then they say, “Well, I want to see the paywalled stuff,” and then they end up going there and purchasing a subscription.

Pete Pachal (37:55)
I guess the trick is always, how do you tease the paywalled stuff? And this is an age-old problem, right?

Without giving it away, because you want to essentially have the engine looking for you to know that you have the thing that the person’s looking for, but you don’t want to give the thing away even to the engine—or at least be assured that the engine will understand that that needs to be preserved.

Michael Ellis (38:20)
Exactly. Exactly.

We try to work with platforms that are at least focused on producing some incremental solutions there.

And I think Rob at AskNews is a good example of that, you know, and they’re developing new elements to their system as well, I think, around looking at certain sources potentially as premium sources, or sources that may be good for a particular use case and could be further monetized in different ways.

Pete Pachal (38:31)
Mm-hmm.

Pete Pachal (38:51)
Well, this honestly steers me into this a little as we start to wrap up.

A couple of last questions around regulation, enforceability, sort of the cause.

What sort of happens in the hypothetical scenario I’m talking about is that the information gets out there somehow. Maybe it’s reproduced somewhere else, or maybe a less scrupulous actor scrapes the content somehow and then represents that to either the world or specific customers, right?

Which is sort of this big issue now with these information brokers that are ostensibly sort of making a bot-friendly internet.

We don’t have to get into all the details of that.

But I guess my question to you is: What are the most direct things we can do to sort of get a grip on that idea?

Even for the people who play in legit services and with the rules, there’s this gray-to-black market over here that is just so damn easy to go to and get what you want for a steal, ostensibly.

Maybe not a steal. Maybe it’s more simplicity than price, but you know what I mean.

So structurally, how do we ensure the market tends toward a more legit iTunes/Spotify model than the Napster model, to use a very crude analogy?

Michael Ellis (40:18)
Yeah. Yeah.

You know, I mean, in every way that we can make incentives for people to do the right thing, right? And disincentivize people that are doing the wrong thing.

And that can happen in a lot of different ways.

There’s just, at the level of mission, the connections that we try to make as an organization with partner organizations.

For example, we work with a lot of copyright orgs that are in there defending publishers, representing them, making sure they’re in the conversation.

Newstex does it itself by representing, I would say, the perspective of smaller publishers.

So you have kind of that level of advocacy.

Then you actually have information and things like price discovery or attribution.

So the folks at ProRata we’ve talked about—you had, I keep mentioning all these guests, but I’m telling you, I connected to them through your pod.

Pete Pachal (41:14)
Glad you’re a fan, dude.

Michael Ellis (41:17)
Jonathan Wone at Kashmir is doing a lot around price discovery because he’s got a lot of information there as, once again, kind of a middle layer in the market.

So, I mean, I’ll mention another one. There’s a company called Miso AI. I don’t know if you’ve heard of them. They would be a great guest.

They’re doing a lot of forensic analysis, actually, about infringing, potentially infringing—

Pete Pachal (41:19)
Yeah. He’s so smart. Yeah.

Pete Pachal (41:37)
Mm, sure, yeah.

Michael Ellis (41:47)
—material online, but also—

Pete Pachal (41:50)
Coming soon to The Media Copilot podcast.

Michael Ellis (41:47–42:17)
—but also trying to actually find ways to, through evidence and data, say, for this reason, this piece of content should command more money in this market or in this particular use case.

So you have advocacy, you have people actually operationalizing technology in order to help fight back.

And then I think also business model is important.

I think publishers are really used to kind of competing with each other, to be honest with you, or being, let’s just say, kind of their own islands.

And culturally, I think—and I just think that the thing about technology and tech companies, they have ways of—

Pete Pachal (42:32)
Yeah. It’s very—it’s almost cultural, for sure.

Michael Ellis (42:44)
—these network effects kind of encourage concentration, in my opinion.

And you have this dispersed group of publishers that is somewhat disorganized with a quite organized small group of tech companies, comparatively.

And so I think business models that bring publishers together and help them to level the playing field, or at least push back as one force, one body, one unit, let’s say through something like ProRata’s federated model of content or what Newstex tries to do in its own way by gathering all of these smaller publishers together, we can also kind of command more market power for the publishers.

And I think help them get through what might be a little bit of a rocky time here as AI finds a new basis for the advertising industry, which I think is kind of at the core of a lot of publishers’ concerns right now.

Pete Pachal (43:42)
Okay, you could say that. It’s a rocky time.

Last question’s always the same.

As you look out into the AI-mediated future, what is one thing you are concerned about, keeps you up at night, and what’s one thing you are hopeful about?

Michael Ellis (44:00)
Well, I’ll start with the bad news first, because it’s related to what I just said.

You know, I mean, I’m an optimist. I do think that this will eventually work itself out.

But how do you make it past this really rough time?

That’s my worry, is that these efforts at the level of business model, or the effort to operationalize what is, in effect, a new way of pricing advertising on ProRata, other efforts maybe politically to lobby for publishers’ interests or things like fair use—I just wonder if it’s going to happen quickly enough.

And that kind of keeps me up at night because I think you have to be ready when the tool becomes available to come together as an industry.

And I’m not sure that the pieces are in place there for publishers to come together in that way, on those different levels I was just describing.

So that’s what keeps me up at night.

I’m hopeful about—I think, you know, again, I kind of think of things from the perspective of smaller publishers.

I am hopeful. I do see the technology actually delivering pieces of information coming from smaller publishers that are finding their way to someone who’s posing a question to an answer engine.

And they’re picking up a new audience or brand presence, even—

Pete Pachal (45:39)
Okay.

Michael Ellis (45:41)
—even though they don’t necessarily have the same marketing budget of a bigger-name publisher.

And so I see a lot of potential there for the long tail of smaller publishers that we represent, given the power of this technology.

Pete Pachal (45:57)
Nice. We’ll leave it there.

Michael Ellis, thanks for dropping by The Media Copilot.

Michael Ellis (46:01)
Thanks, Pete.

The post The future of AI content licensing, featuring Michael Ellis appeared first on The Media Copilot.

If you’re a publisher that makes money through advertising, you already know the bot math hasn’t been working in your favor. Crawlers show up, chop your pages into machine-readable chunks, and hand the substance over to an AI system’s answer, usually leaving nothing behind for you. With bot activity surging as human referral traffic keeps sliding, blocking as many crawlers as possible has become the default defensive posture for most publisher sites.

Time thinks there’s a better move: stop treating bots purely as a threat, and start treating them like an audience you can sell to. The magazine has begun serving ads specifically meant for bots, an attempt to monetize what is arguably every publication’s fastest-growing readership, machine or otherwise.

Here’s the mechanism, as reported by Digiday: Building on its work creating machine-readable versions of its pages, Time is running what is essentially an advertiser-directed FAQ section, tailored to answer the kinds of questions people ask AI search engines about the brand or its products. Those FAQs stay invisible to human readers; only the crawlers see them, and when they do, the content is, hopefully, repeated in some form in the summary that shows up for the person who asked the question.

Time is reportedly charging for one agent ad per machine-readable page, priced as premium inventory. Every crawler fetch becomes a countable request, which functions like an impression metric, minus the human on the other end.

The cleverness here is in the reframe: By treating the scrape itself as an ad impression, the publisher can sell its machine pages as inventory. But what the advertiser is actually buying is a maybe—the chance that an AI system retrieves the sponsored material and works some of it into an answer. No guaranteed placement, no guarantee the message survives the trip at all.

For publishers, that reframe does something useful: it moves the expectation of payment off the AI vendor, who was never going to pay anyway, and onto the advertiser instead. Getting scraped, ironically, becomes essential to the model. Without it, none of this works.

Does the bot know it’s an ad?

Disclosure is a sticking point, and it’s the part every comms and marketing person should be watching closely. People don’t love ads, but if they need to be present, they want them labeled so it’s clear what’s editorial and what’s paid. Time discloses its bot ads, but a label only works if it survives the trip from source page to AI-generated answer, and depending on the query, and how well the ad lines up with the surrounding content, the sponsored material might get folded right into the response with no seam showing.

In other words, disclosure can’t just be a tag sitting on the page. It has to travel as data. The model has to recognize the material as commercial, hold onto that status as it processes the page, and surface it to the user when it shapes the answer. No AI system is currently required to do any of that.

Time is working with Mobian, an adtech platform, on its bot ads, but it’s not alone. A company called Oasy takes a more aggressive version of the same approach: its technology inserts an advertiser message aimed squarely at the bot, invisible to any human reader. I’ve tested Oasy’s publisher software myself, and it gives publishers a clean count of bot requests and ad impressions.

That invisibility is exactly where publisher-side bot ads part ways from what ChatGPT and Google are doing inside their own AI products. Both of those emphasize clear labeling and a hard separation between the ad and the answer. There’s a real irony in that: Big Tech’s approach to AI advertising currently draws a cleaner line between editorial and commercial than publishing’s own does. But it’s not hard to see why publishers landed here. Pay-per-crawl and pay-per-use licensing hasn’t produced meaningful revenue industry-wide, and licensing deals are mostly reserved for the biggest outlets. Everyone else has to get creative about monetizing bots, disclosure risk and all.

The bigger difference, and the one that should matter most to anyone buying these ads, is what each model is actually selling. Advertise in ChatGPT and you get a clearly marked placement, guaranteed, on a platform with enormous reach. A publisher is selling something else entirely: authority. If AI systems broadly treat a publisher’s content as authoritative, that authority travels across engines: ChatGPT, Gemini, Claude, AI Overviews, Perplexity, all of them. Different engines weight things differently, and licensing deals still matter, but the value of an authoritative post, author, or outlet can get amplified across the AI ecosystem even when the human audience behind it is small.

The agentic ad market

Here’s the part that should really get the attention of anyone in marketing: the bot audience is bigger than the crawler traffic you can already see in your logs. As agentic use grows, agents spin up subagents to go research things and report back. AI search runs plenty of “fan-out queries” too, searches on related topics a human user never sees. Bots, in a lot of these cases, aren’t just fetching content for a human. They’re the actual audience for the query, with the human downstream as the eventual reader and the bot making the first cut on what matters.

That raises a set of questions nobody has good answers to yet. Will disclosure survive intact through that chain? Even if it does, might the bot judge the sponsored content relevant to the query anyway and use it regardless? How does a system even communicate that part of an answer is commercial? And if the user’s goal is to take an action rather than just get information (booking something, buying something, picking a tool), could an ad tilt an agent’s choice before a human ever sees the alternatives?

The ethics are murky, and so is any advertiser’s ability to trace what happened to their message. An AI system might retrieve it, paraphrase it, drop it, or blend it into something else entirely, leaving the advertiser with no stable creative, no guaranteed placement, and no reliable way to measure any of it. The industry would effectively be selling influence over a recommendation without being able to show exactly how that influence showed up.

Nobody knows yet how the AI companies themselves will treat this model, either. They might view it as clever and let it ride; it could even relieve some of the pressure publishers have been putting on them to pay for content directly. Or ad-supported platforms like Google and ChatGPT could see a competitor and train their systems to filter or downrank pages running bot ads, treating bot-only promotional text as cloaking, spam, or an attempt to game retrieval.

The authority trade

Give Time credit: this is a genuinely new advertising model, and it drops the fantasy that AI companies will eventually just start paying for what they scrape. But the trade underneath it is a delicate one. Publishers are monetizing the very authority that makes their content useful to AI systems in the first place. Weaken that authority with too much bot advertising, and the inventory backing it loses value along with it.

Media companies have plenty of practice walking that kind of line. What’s different this time is that the generative systems on the other side of the trade are a wild card publishers don’t control. Nobody in this equation gets to set the AI companies’ rules for what counts as legitimate content versus manipulation. Ads for bots look like a real shot at new revenue, as long as the systems that make that revenue possible don’t quietly move the goal posts underneath it.

A version of the column appears in Fast Company.

The post Time starts selling ad space that only bots can see appeared first on The Media Copilot.

By The Copilot

What if the future of AI in media isn’t replacing storytellers, but giving them better tools to tell better stories?

This episode is sponsored by Descript.com 

In this episode of The Media Copilot, host Pete Pachal sits down with Laura Burkhauser, CEO of Descript, to explore how artificial intelligence is reshaping media production without sacrificing creativity, editorial judgment, or human storytelling.

Laura explains why Descript’s vision has never been about replacing editors, but about eliminating the repetitive, time consuming tasks that slow production. From text based video editing and AI assisted collaboration to intelligent clipping and personalized workflows, she shares how AI is becoming a creative partner rather than an automated replacement.

The conversation also examines why memory, personalization, and collaboration may become AI’s greatest strengths, how media organizations are adapting to an increasingly creator driven landscape, and why the next generation of production tools will focus less on software features and more on helping teams create great content.

Finally, Laura offers a refreshing perspective on the future of AI, arguing that the industry has spent too much time selling fear instead of showing creators how these tools can unlock more original storytelling, more creative freedom, and entirely new ways of producing media.

The conversation explores:

• Why AI should replace repetitive labor, not human creativity
• How Descript evolved from a text based editor into an AI powered production platform
• Why collaboration remains the most valuable feature in modern media workflows
• How AI memory is creating more personalized creative assistants
• Why editorial judgment, taste, and storytelling still belong to humans
• How newsrooms and media companies are producing more content with smaller teams
• Why the future of media production is conversational, collaborative, and AI assisted
• How creators can use AI to spend less time on tools and more time telling better stories

Why this matters

Artificial intelligence is transforming every stage of media production, but the biggest opportunity isn’t replacing creative professionals. It’s removing the friction that keeps them from creating. As newsrooms, marketing teams, and independent creators face growing pressure to produce more content across more platforms, AI has the potential to become an intelligent collaborator that accelerates production while preserving human judgment, creativity, and authenticity. This conversation offers valuable insight for journalists, marketers, content creators, media executives, and anyone navigating the future of AI powered storytelling.

🔗 About the 👤 Guest

Laura Burkhauser is the CEO of Descript, the AI powered audio and video editing platform used by creators, media organizations, podcasters, and enterprise teams around the world. Under her leadership, Descript continues to pioneer AI assisted media production, helping storytellers simplify complex workflows while keeping human creativity at the center of the creative process.

LinkedIn: https://www.linkedin.com/in/burkhauser/
Descript: https://www.descript.com

This episode is brought to you by Descript.

Descript makes editing podcasts and video as easy as editing a document. Record, transcribe, clean up audio, remove filler words, create clips, and more with powerful AI tools built right into your workflow.

Spend less time editing and more time creating with Descript.

Visit Descript to learn more.

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

THE MEDIA COPILOT TRANSCRIPT

THE MEDIA COPILOT

AI Won’t Replace Creators

Featuring Laura Burkhauser, CEO of Descript

[00:22] Pete Pachal:

Hi, welcome to this sponsored episode of The Media Copilot, presented by Descript. 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 trying to answer 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 this week is Laura Burkhauser, CEO of Descript.

Every media team right now is trying to answer a set of questions that are straightforward, but the answers are increasingly difficult. The main one is: How much AI should we bring into production?

That question alone leads to many more questions. Which parts of the workflow are safe to automate? How do we use these tools without flattening voice, lowering quality, or losing editorial control? And how do we avoid stacking one vendor on top of another just to record, transcribe, edit, clip, caption, summarize, and publish a single piece of media?

That last part really matters. A lot of newsrooms, comms teams, and branded content teams aren’t suffering from too few tools. They’re suffering from too many. One tool for recording, another for transcription, another for editing. The list goes on.

The promise of AI is supposed to be that you can go faster, have better quality, and have more leverage. But in practice, it can also create more complexity, especially if your workflows aren’t entirely thought out.

That’s why I’m excited to talk to Laura today. Descript is one of the companies trying to rethink what media production looks like when AI is actually built into the workflow rather than just bolted on afterward.

So this isn’t a conversation just about one product. It’s a conversation about how audio and video production are changing, how bigger teams should think about workflows, and how the interface for making media may become less about individual tools and more about what teams want to create.

[02:30] Pete Pachal:

We’re going to talk about where Descript fits into that shift, what newsrooms and media organizations should know if they haven’t used it or looked at it in a while, and what responsible AI looks like when the output isn’t just that one single piece of content.

Laura Burkhauser, welcome to The Media Copilot.

[02:46] Laura Burkhauser:

Hi, Pete. Thanks so much for having me.

[02:49] Pete Pachal:

Sure, it’s my pleasure. Let’s get into it. Let’s get into Descript.

As I understand it, it started as a very specific idea: editing audio and video via text. Now it feels more like a full production system. How would you describe the company’s evolution, and what has changed the most since generative AI became central to the product or integrated into the product?

[03:12] Laura Burkhauser:

Yeah. So Descript has been AI native since we were founded several years ago, and that’s because the concept of editing video like text is itself AI. Editing video like a document is itself AI.

The way that Descript works fundamentally, or our core feature several years ago, was that you record something like this. It’s going to be a 40-minute-long video podcast.

In the old world, you would go into a timeline and do a lot of cutting and splicing, a lot of work on audio, and cutting out all of my filler words. Well, many of them, maybe not all of them.

If you talk to sound engineers, they’ll tell you, “I know what that looks like on a timeline. I could draw it for you.”

[03:55] Pete Pachal:

And manually doing it too, literally putting little spaces and stuff.

[04:08] Laura Burkhauser:

Exactly. So Descript said, “Well, what if you could edit things like you edit a document?”

That doesn’t just mean deleting filler words or deleting things like retakes, but also copying and pasting and moving things around. That was kind of the initial idea.

Then Descript acquired a company called Lyrebird, which did AI audio generation, and brought that in as a research team to bring more AI into the product.

If you think about the things you do with a document, you can cut, you can copy and paste, and you can also write. We were one of the first products to provide text-to-voice, where you can write new paragraphs. Or, in my voice, if I use the wrong name or you pronounce my name wrong, you can actually regenerate it without having to re-record.

Those are some of the original features that we went to market with, and we immediately achieved product-market fit, especially in media.

Some of our longest-running customers are actually customers in some of the biggest, most noteworthy newsrooms that you know. That’s because, in addition to editing video like a document, we also had collaboration from the very beginning.

It’s like editing video the way you would edit a Google Doc, right? Someone in your newsroom may know the hell out of the story, but you have someone else who’s really a legal expert and understands the things that you can and can’t say. You have an editor coming in and checking the work. You have a copy editor coming in.

They all bring a different amount of expertise into this rough cut, eventually fine cut, and eventually polished step that you have toward making video.

Collapsing all of that collaboration together into a document that everyone can see, and allowing people to do things like paper edits, tremendously shortened the amount of time it took to get from recording to publishing.

That’s the foundation upon which Descript was built and why I think, to this day, we are in most newsrooms. In some, we’re wall-to-wall. Every single person on the production team has a Descript subscription.

Now, you asked what has changed because technology has changed so much since that initial promise. The biggest thing is LLMs.

[06:34] Laura Burkhauser:

What’s great is, if you’re able to edit video like a document and then LLMs come along, what are LLMs incredibly good at doing? Editing text.

Well, we understood all video as text. So having an LLM come in and be able to edit text really well kind of gives you multimodal editing for free.

[07:01] Laura Burkhauser:

Then adding captioning to all of our videos, so you know what’s happening on the screen every few frames, gives you visual understanding.

A dog walks by, and we’re able to say, “dog walks by,” and store that as text. That gives you visual understanding in a real multimodal way before you can build a multimodal model, which takes hundreds of millions of dollars and hundreds of researchers.

Instead, we were able to get that to you early, a couple of years ago now, which was way ahead of the times.

Then last year, we built an agentic co-editor, Underlord. Because nobody needs an AI overlord, but couldn’t you use an AI Underlord?

Underlord can come in, take direction from you, and take the first pass at editing.

[07:45] Pete Pachal:

Hope it doesn’t ever vie for a promotion.

But that all sounds really fascinating. The bets that you placed early ended up really paying off in the AI era because of how language models work.

Are there specific features that editorial teams have really zeroed in on and that explain why it ended up being popular for them?

I imagine it probably varies a bit by the type of newsroom, whether you’re more broadcast versus publishing, but give me some thoughts on what really resonates with that crowd.

[08:20] Laura Burkhauser:

Originally, text-based editing itself was huge, and collaboration, which isn’t an AI feature, but it’s just a great feature. Being able to all edit at the same time, leave comments, do approvals, things like that.

But then, as video repurposing became more important, turning one piece of media into the 17,000 pieces of media that you need to feed every single content maw across all of the publishers, using us for clips has been really exciting for people.

We have a button that creates clips, but clips is actually the feature that forced us to build Underlord.

When we released clips, we had a bunch of different parameters you could set. You could say, “I want this to be vertical. I want the clips to be about 30 seconds. I want you to use this layout that I’ve already created with my brand stuff and look for clips about this subject.”

Those were the parameters you could add in.

Immediately, people had a bazillion other parameters they wanted to add. They were like, “I want to create clips for every single one of my channels at the same time. When you do it, I want you to actually create a social post about each of the clips at the same time. I want you to only choose clips where my guest is speaking. Don’t include me in the clips that you’re adding.”

And I’m like, well, this is so much direction that at this point we’re not talking about buttons anymore. You need a little Underlord to yell at.

That kind of led us to create this idea of Underlord, which I think people really like.

Now Underlord has memory. So you can work with Underlord once and say, “Look, when I say go out and create clips, what I actually mean are all of these types of clips, along with this definition of a publishing packet that I want you to create as part of that.”

Now every time I tell you, “Go make clips,” I want you to do those 50 different jobs that we got right once.

That totally changes your workflow for some people.

You’re right, it doesn’t work for everyone. But you can really have Underlord do the first pass at everything. Then you go in and act as a reviewer.

I think for a long time, and probably honestly forever, when we’re talking about things where voice is really important, you go in and change and shift things and add in the layer that makes it you.

But Underlord can do the perfunctory things that you might hire an intern to do.

[10:44] Pete Pachal:

Nice. So, yeah, clipping is obviously huge these days, and it sounds like that’s sort of the core of maybe your value proposition to a lot of these media companies.

But you tell me. When you boil down why someone would use your tool versus something else or a stack of things, is it that multimedia capability? That you can make more from a single piece of content more easily, both in terms of speed and maybe in terms of the intelligence behind how it does it?

[11:21] Laura Burkhauser:

It really depends on the customer.

In actual newsrooms, if you ask people what they love about Descript, it’s not one-shotting editing jobs with generative AI. It is a fully functioning collaborative editor that uses AI to speed up the parts of your job that you don’t like.

My vision for newsrooms isn’t that you should just go from recording to one-shotting everything.

A lot lives in the editorial decisions that go into the rough cut. If you think about a long-form audio podcast that you love, the obvious one might be something like This American Life, but there are a million now.

There’s so much audio that’s not just a straight show like this. You and I are doing a chat show.

[12:18] Laura Burkhauser:

Editing this is going to be pretty easy for your editor, and a lot of the magic comes from the interview that you and I are doing.

I don’t know how complex you get in your editing job, but my guess is this is a pretty quick editing job. Speed is what I would use to sell this to you. Speed and repurposing all the clips is what I would focus on if I were selling to your editor.

If I’m selling to NPR and they’re doing a lot of really high-quality, prestige podcasts or video, there I’m talking about the depth of the editor. I’m talking about having your whole team in there.

Yes, we have AI features that make things way easier to do and save you hours and make you able to create a daily show instead of a weekly or monthly show.

But every story I’m telling is about humans doing a lot of work in the editor. And I think that’s because, to create really high-quality stuff, you just need humans doing work and making decisions.

That’s just something I stand by.

[13:13] Pete Pachal:

Right. So you have that inherently collaborative nature of the product, and now you have Underlord, which is kind of a collaborator too.

[13:20] Laura Burkhauser:

That’s exactly what I think about it as.

Underlord is another editor in Descript with you that is able to do some editing and has its own area of expertise, which is not the same area of expertise that you might have, that Steve from Legal might have, or that your copy editor might have.

Thinking about Underlord not as someone that you’re delegating work to in all cases, I think for some people it can be that, but in newsrooms, Underlord is more like a collaborator.

[13:52] Pete Pachal:

I’m glad you brought up memory. I’d like to chat a little bit about today’s models and how the features and capabilities overall are really accelerating.

I’d like to know, not just what helps Descript, but broadly what you’re excited about as things are progressing.

Memory, of course, has been around for a while, but it’s been rapidly evolving. That just seems like a key way things will evolve.

We think about memory as personalization for you, but in the case of a tool, it’s personalization to the workflow and someone’s rhythm. The way one person uses the tool might be totally different from someone else’s.

Talk a little bit about memory and how the new models are evolving. What excites you the most?

[14:52] Laura Burkhauser:

I think memory is really exciting, and I’ll tell you a little bit about how it shows up in creative software like Descript.

One of the fundamental criticisms of AI is that AI is, at its heart, a derivative piece of technology.

The way that it works is it does a good job of predicting what might be a good thing to do. And the way that it predicts is it trains on a huge amount of data.

That is a terrible way to create, right? What if everything you created was fundamentally derivative?

To some extent, I guess you could say you go through life and take in all of this data and have these experiences and work on a voice. And out of that comes, we hope, as creators, something that’s interesting enough that it stands out.

[15:50] Laura Burkhauser:

AI on its own will never do that because it’s fundamentally derivative.

However, what memory can do over time is begin to pick up on some of the nuances of the way you work, the context that you’re in, the workflow, and how you make things.

It stops being this regression to the mean of the most boring way to edit a podcast, tell a story, write a document, or paint a picture.

It can understand Laura’s voice, Laura’s priorities, the beats that Laura loves to hit in her stories, the importance of authenticity and humor in the way that she comes through. That might make you make different editing choices than you would make for Pete and his storytelling.

When Underlord can get a sense of your voice and your taste and your perspective, I think you start to get more excited about collaborating with it, the same way you would with a real editor whose job is to help your voice and your vision shine.

That’s why you see the same creative teams work together over and over again.

Great, your collaborator gets it now. You get the vision, you get the context, you get the kind of thing I’m trying to make. Let’s go make four more of these because you get it now.

That’s the excitement of memory for me in creativity.

[17:18] Pete Pachal:

Nice. It sounds like, in terms of it being a collaborator, it’s not even necessarily a collaborator just for people who are professional editors.

The whole text-based editing concept is a bit of a mental shift, but it’s making video editing capability, and just broader multimedia capabilities, possible for people who aren’t professional editors.

[17:44] Laura Burkhauser:

Exactly.

[17:47] Pete Pachal:

How do you think about that in terms of the ongoing discussion in society around AI? Is it here to help us? Is it here to replace us?

Does it help make teams more lightweight? Does it support them? Does it move certain lightweight tasks onto nonprofessionals? How do you think about all of that?

[18:15] Laura Burkhauser:

I think we need to absolutely throw out this concept of AI replacing humans, wholesale. I don’t want to hear about it anymore.

I’ve been saying this for a while. I think now it’s finally catching some steam, so I feel less sheepish saying it in San Francisco.

I do think AI will replace some labor, which is very different from saying AI will replace humans.

The way that I think about it, the way that I’ve worked with my team on it, and the way that I would encourage creative people to work on it, because you will come up with different answers than what me and my team came up with, is to list out the labor that you do to get to the product that you make.

Do you like doing all of that labor? My guess is you don’t.

Let’s separate it into a couple of buckets.

In the bucket of labor you don’t enjoy doing, how do you think about delegating this to AI?

You can’t always do it. I too would love to delegate putting away my clothes to AI, but unfortunately I can’t. That’s a human job, probably forever.

But how can I use AI to replace my labor with its labor here?

Then, for the stuff that I enjoy doing, how can I use AI to enhance my ability to do this labor?

[19:41] Laura Burkhauser:

I think there’s an enhancing bucket and a replacing bucket that probably follows patterns along different functions, but isn’t the same for every single person in the same function.

AI can help on both sides of the equation, but we have a choice.

We have a choice as individuals. We have a choice as organizations. We have a lot of choices for how we use AI.

I encourage everyone to use AI because I think it is foolish to say, “Hey, we have this thing that could make you so much more productive, and you’re just not going to use it on principle.”

To me, that feels like, “I don’t own a computer. I prefer a typewriter.”

Sure, as an affectation, that’s hilarious, but come on. No one actually thinks it’s a better way to work.

I think AI is going to be part of every non-affectatious life.

[20:39] Laura Burkhauser:

But I think how we use it is really going to depend on the function and on the person, to some extent.

And I don’t think it’s going to replace a single human.

[20:46] Pete Pachal:

Right. It’s more like you say, labor, tasks, whatever the word is.

To my knowledge, and I don’t have the data in front of me, the data is kind of supporting that.

I would be curious, though, about what you’re seeing, whether it’s data or anecdotes or whatever, with regard to the organizations you work with.

As they use your tool over time, do you find they’re doing more things that they weren’t able to do before? Do the teams start to get reorganized a bit?

What happens over time as people get acclimated to both the tool and working with the AI features?

[21:36] Laura Burkhauser:

Yeah, that’s a great question.

First, the way that I would talk about Descript’s customers is that we work with media teams and marketing teams.

We work with media teams all the way from a solo podcast to a national or international newsroom.

We work with marketing teams all the way from a solopreneur who is creating content to market their coaching business to some of the biggest enterprises in the world.

We work with storytellers in media and marketing. Those are our main kinds of customers.

What we find that they’re able to tell us is that, because of Descript, people who normally cannot take part at all in the media editing or storytelling part of the equation are now able to help.

But really, the story is that we can do video where we couldn’t do video before.

We hear from places saying, “We stopped doing video as a team because it was just too much. It was too much work. We were doing all text and maybe some audio, but no video.”

Now, we can do video as a content type.

Or we moved from doing video once a month to being able to do a daily show.

If we’re talking about an actual newsroom, doing a daily show is insane. The fact that you can do multiple daily shows and just have one or two editors working on that is unthinkable in the podcasting world of a decade ago.

And that’s because of Descript.

We can do it more often. We can do video where we couldn’t. We can do video more often.

[23:32] Laura Burkhauser:

And we can do video across many more channels.

We can do long-form video and all of our short-form clips. We can do video that is tailored for all of these different channels where we need to go out and meet the customer because they aren’t necessarily coming to our site anymore.

What I actually see is a lot of media companies moving toward a world where, yes, they have their own site and their own channel, but they also have a network of creators that you may never know is part of that media conglomerate.

They work with all of these podcasters or creators who are monetizing the way creators monetize, through things like sponsorships and YouTube ads, and the media company gets some portion of that.

There are these new economic models being built on a creator economy that depends on being able to create video much faster and across many more channels.

[24:32] Pete Pachal:

That sounds really interesting because it dovetails with a lot of media strategy right now. It’s taking advantage of this creator economy, the systems, and the ways that creators execute.

It’s an interesting merger. Obviously, like you say, creators are doing it, but even the bigger companies want to take that approach.

[24:57] Laura Burkhauser:

They absolutely are.

That’s critical to more traditional media companies surviving in the age of new media.

You want to keep the brand recognition that you have. You want to keep the valuable asset that you have. But you need to start going out and meeting people where they are, across all of the channels where they’re consuming news, which is not necessarily your site.

[25:19] Pete Pachal:

Of course, bigger companies, when you get to the enterprise level, have a set of concerns around liability and security and all these other things.

Has that been a challenge at all, trying to address all those things they want to do, like SSO or whatever?

[25:36] Laura Burkhauser:

Every startup loves going enterprise. It’s everyone’s absolute favorite thing to do.

As a product manager, it is always hard for me to say, “Yeah, I don’t want to go build agent memory because I would so much rather build SCIM,” which is a permissioning solution for companies that need to let some teams have access to some things and some teams not have access to other things.

If you had asked me two years ago, I would have been pulling my hair out about that problem.

I think we’ve mostly crossed the chasm there and are able to offer mature enterprises all of the wonderful features that they would love.

We’re also very careful about things like data retention. An enterprise company is obviously very private about their data.

Making sure that we’re able to meet customers where they need to be met when it comes to things like data privacy, permissioning, and all of those other wonderful enterprise features is important.

[26:49] Pete Pachal:

If you don’t mind getting into the weeds a bit, I’d like to ask about some of the features that ended up being surprisingly important.

As I edit things and do my amateur-hour stuff on podcasts or whatever, depending on your passion for the material, you can end up being kind of a perfectionist about things like chapters or show notes.

Particularly with AI features, the promise is always that it gets you a certain percentage of the way there and then the human takes it from there.

Has there been something where, surprisingly, people really needed it to be 95% there where you thought 70% or 80% would be enough, and then you worked to perfect that?

There’s a lot of iteration on some of these creator features, I imagine, depending on the feedback you get from customers. Is there anything that stands out or was surprising?

[27:56] Laura Burkhauser:

That’s a good question.

I’m always humbled by customers. It’s one of the things that I love about this job.

You have an idea of what people will use a feature for and how it’s going to land, and then the data always humbles you. The interviews always humble you.

I’ve been surprised by many features.

For me, maybe something that was surprising was when I was an amateur podcaster. I actually found Descript through podcasting.

I started a podcast with my friend, and we used Descript to edit it. I was like, “My God, I think I have to work at this company. This is the coolest thing I’ve ever used.”

[28:42] Pete Pachal:

You didn’t tell me you were a professional at this.

[28:47] Laura Burkhauser:

I am definitely not.

But one of the features that I loved was Remove Filler Words because, if you can’t tell, unless Pete has kindly removed all of my filler words, I use a few filler words.

[28:58] Pete Pachal:

I like to keep it real.

[29:16] Laura Burkhauser:

Exactly.

So I was removing all my filler words, and then as soon as I joined Descript, I found out that there is a huge camp of podcasters who are pretty against removing filler words and actually pretty against a lot of the automated editing, just-one-click, don’t-think-about-it kinds of buttons that we created, like removing pauses.

They’re like, “Look, when you get further into the editing game, you will realize the value of a pause and the value of a filler word, and of retaining the integrity of the sentence the way that the person created it.”

You don’t always do that. You never want to embarrass your guest. Do you hear me, Pete? You never want to embarrass your guest.

[29:50] Pete Pachal:

Wouldn’t dream of it.

[30:10] Laura Burkhauser:

You do want to get rid of some of them, but it takes judgment.

The more you talk to people who have been in the editing game for a long time and who are really working on S-tier kinds of content, the things that you love and have a ton of admiration for, the more you realize how much judgment goes into even something like that.

Which “ums” do you remove?

Automation can be really helpful for a lot of kinds of content. But that’s where I don’t lean a ton on automation when I’m talking to an experienced editor because it just doesn’t ring super true to them.

[30:34] Pete Pachal:

It reminds me of when Ben Affleck had that thought a couple of years ago about AI not having taste.

I wholeheartedly agree with everything you said there about integrity. Sometimes you want it out, sometimes you don’t, and that could even be in the same piece of media.

By way of transitioning to looking forward, do you think we’ll ever get there with AI?

Could it look at a conversation and think, “A pause right there would be great,” and then leave something in and apply a certain amount of taste to it?

This is getting pretty advanced, but who knows? New models come out every week.

[31:17] Laura Burkhauser:

I think yes. And that’s what we’re aspiring to build.

But again, I think that taste is not generic.

When we talk about taste, we talk about it as either you have it or you don’t. I think that’s true up to a certain bar, and I think we can get AI up to that bar.

But then the question is: Is it interesting?

[31:47] Laura Burkhauser:

Is it authentic? What is the taste? Taste to what end?

That’s where it’s all about who’s driving the car.

I do think it will get better. I think there are also going to be limits.

When it gets that good, will it get that good in a way that works with the business? Will it get that good in a way that is cost-efficient and works across all tools?

It’s not just intelligence that is the bottleneck here, but certainly the level of intelligence is a bottleneck.

I think taste is eventually a solvable problem, but it won’t fundamentally change the fact that taste plus an excellent human will still outperform generic taste every time.

[32:39] Pete Pachal:

Yeah, that makes total sense.

I’d love to chat a little bit about the general trends in AI these days. There’s been a bit of a backlash here and there.

I know that’s a generalization, but people are getting booed at graduation speeches. Whether it’s generational, I don’t even quite know how to think about the various dimensions of this, but there’s no doubt there’s a phenomenon out there.

Thinking about the people you talk to and creatives, who have always had a segment that has been skeptical of AI, how do you hear all that criticism?

What do you think is fair? What isn’t?

And what do people, particularly at AI companies, get wrong about how they talk about AI, particularly to this demographic of creatives, writers, editors, et cetera?

[33:34] Laura Burkhauser:

I think the way people talk about AI in San Francisco is absolutely insane and so alienating and inaccurate.

People are always inaccurate, but it feels self-defeatingly inaccurate in a way that, if you have any kind of connection to reality, if you have relatives who don’t live here that you respect and talk to, you’re just like, “What? I’m sorry?”

Why is the leader of every lab walking around saying, “Someone should stop me. My God, I’m building this really dangerous thing that’s going to ruin democracy and kill everyone. Someone should stop me,” and then is surprised that people are like, “I’m super freaked out. You should stop building this. This is a bad thing.”

It’s like, you’re giving us the talking points, dude.

Stop telling us that you’re building bad things. You’re freaking everyone out.

I just don’t understand it. I kind of understand it. You want to feel like, “Wow, everyone should give me more money because I’m building this thing that’s so amazing that it might kill us all.”

I still think that’s questionable. Please stop doing it.

You’re not watching this podcast. You’re very busy. But maybe someone can show this person a clip.

Stop doing it. Stop saying that.

[34:50] Pete Pachal:

I think their comms teams are listening to this podcast, so hopefully that’ll get through.

[34:55] Laura Burkhauser:

Yeah. Stop doing that.

I got started in consulting, and we used to have to take trainings on change management.

Whenever there was going to be a big change, they’d say, “You’ve got to identify the burning platform and you’ve got to identify the beach.”

The burning platform is the reason why you absolutely have to change. We cannot stay the same. It’s not going to work.

The beach is the party that you get to have when you finally jump off the burning platform. This is the thing you get people excited about.

No one is selling the beach right now.

No one is telling us why we’re excited about AI.

To the extent that they are, they’re using language that makes capital owners excited, like “more productivity.”

But to someone doing the work, when they hear “more productivity,” they hear, “Wow, sounds like I’m doing a lot more work in the future.”

Or you have people talking so far into the future that it doesn’t feel credible and isn’t credible, like, “There won’t be any work in the future.”

That’s scary, too far away, and probably not ever true in anyone listening to this lifetime.

I just think we’ve gotten it totally wrong about getting people excited about this stuff.

[36:18] Pete Pachal:

So then, zeroing in on the creative set, your customers, how do you think about that in your messaging?

How do you talk about AI with customers or even just people in general?

[36:32] Laura Burkhauser:

In the creative world, it’s even worse because you have this Twitter feed of some of the lamest stuff you’ve ever seen with AI-generated video.

Then you have a bunch of people in the tech world being like, “My God, Hollywood’s dead.”

Anyone who actually makes video is looking at this dog surfing and is just like, “I’m super unimpressed by this dog surfing video. Am I insane?”

Why is Hollywood dead because you made a pretty mid video of a dog surfing?

This is bad.

[37:05] Pete Pachal:

Yeah. It’s a standalone thing that, on its own, might be interesting and curious.

Show me the movie where that’s somehow integrated and it’s flawless, and maybe we’ll talk.

[37:17] Laura Burkhauser:

Yeah, and maybe we will.

I frankly think that we will start to see generative AI technology in video and in movies in Hollywood. We probably already are.

I think we’ll see this technology come in, and it’s going to be boring.

It’s going to be the way that Pixar changed the way we all do animation, with a pretty boring, to most people, behind-the-scenes change in how animation happens.

No one thinks about that. They’re just like, “Wow, Monsters, Inc. was such a good movie. I really liked that story. It made me cry.”

That’s what gets people excited.

It’s not the technology that people are using. It’s actually making really cool, admirable, exciting art.

When I look at the box office right now and see a movie doing super well that was made for under a million dollars and has already made something like $250 million at the box office, this is exciting to me.

This is a beach.

What if the beach for AI and creativity is that you are empowering people to make really exciting, original storytelling for under a million dollars and get distribution?

More stories get told. More original stories get told.

We’re not going to see just a bunch of $500 million superhero movies in the theater. We’re going to get to see original storytelling and be able to take more bets on early-stage filmmakers.

I’m excited about that beach. These are some of the stories you can tell.

At Descript, because we don’t do a lot of this generative, “there’s no real human in this ad” stuff, we do recorded media.

[39:01] Laura Burkhauser:

A lot of how I talk to people is that I say: Struggle with your art, not with your tools.

We’re not trying to replace the art here. We’re trying to help make it easier for you to make it.

You’re still going to need to struggle with that storytelling, but not with the tools that it takes to build that story.

[39:20] Pete Pachal:

I usually like to end these conversations on things you’re hopeful about and maybe concerned about as we go forward, but I feel like you’ve answered a lot of that in what you just said.

I’d be curious to jump off your last point about making things more accessible because I feel like that zeroes in on a trend in toolmaking.

You see this in a lot of different creative apps. Basically, it feels like everything is slowly moving away from discrete buttons, menus, and hard-to-find features.

If not overtly a chatbot, it’s becoming more conversational. You tell the tool what you want, and it figures out what it needs to do.

How far does this idea go?

Putting aside Descript for a moment, what does interacting with software look like in three to five years?

[40:14] Laura Burkhauser:

That’s a great question and one we talk and debate about all the time at Descript.

I don’t think buttons are going away, but I do think chat is still on the ascent.

I’m a big believer in voice. The way that I talk to Underlord is that I dictate to it.

As you can tell, I’m a bit of a rambler. It’s hard for me to get it into five words. That’s the difficulty with writing for me.

[40:44] Laura Burkhauser:

It’s sitting down and writing what I think succinctly.

So I turn on the microphone and just kind of ramble to Underlord and let it know what I want it to do with the video, and it does it.

[40:54] Pete Pachal:

I feel like the AI question of the moment is, “What do you use to dictate?” as opposed to, “What’s your phone, iPhone or Android?” from the previous era.

That’s the pro move.

[41:02] Laura Burkhauser:

Totally. I use Wispr Flow. I love it.

But I don’t think buttons are going away.

Buttons and dials are important for people who need control and want to get in there and set things the way that they want them.

I’m excited about UI on the fly, where the computer is smart about generating the right buttons and dials at a moment’s notice.

[41:32] Laura Burkhauser:

If it’s easier to turn a dial and I’m talking to you and I’m like, “Hey, I need more confetti in this shot,” maybe it generates more confetti in the shot and also gives me a confetti knob.

It’s like, “Okay, I put in more confetti, but if it’s not right, just turn the knob or dial to the right amount of confetti.”

You would never need a permanent confetti knob, but because it’s so trivial to generate software in the future, it can actually just generate that knob for you at a moment’s notice to give you the control.

Buttons and knobs are really useful for high-level control, which you’re always going to need for precision editing.

[42:05] Pete Pachal:

Nice. Yeah, I think some people might need a confetti knob. A lot of people edit wedding videos, so that could be a thing.

Laura, this has been fantastic. I want to say thanks so much for dropping by The Media Copilot and sharing your thoughts.

There’s a lot to absorb, but this has been great. I really appreciate you coming on.

[42:40] Laura Burkhauser:

Pete, thanks so much for having me. A really great conversation, and I look forward to chatting again soon.

[42:46] Pete Pachal:

For sure.

The post AI won’t replace Creators. It will replace the work they hate. appeared first on The Media Copilot.

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

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

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

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

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

Two moves against slop

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

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

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

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

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

Target content, not process

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

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

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

Filters are easy, calibrating is hard

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

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

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

A version of this column appears in Fast Company.

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

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

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

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

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

Transparency has a price tag

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

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

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

Three forces are pushing the AI byline out.

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

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

What the byline really is

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

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

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

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

Human accountability is the scarce signal

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

A version of this column appears in Fast Company.

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

By The Copilot

Bots now make up about half of all web traffic. Are they a threat to block or an audience to win? 

For most of the web’s history, the deal was simple. Search engines pointed people to websites, and those visits paid for the content through advertising and subscriptions. AI is rewriting that arrangement, and the clearest place to see it is in who, or what, is showing up at the door.

Akamai says it now handles more than 150 billion bot requests a day, with AI bot traffic climbing more than 300% year over year. Bots account for roughly half of all internet traffic. On this episode of The Media Copilot podcast, Pete Pachal talks with two Akamai executives who sit on opposite sides of that shift: Kim Salem-Jackson, the company’s chief marketing officer, and Patrick Sullivan, its CTO of security strategy. Their jobs once had little to do with each other. Now they are working the same problem from two directions.

For Sullivan, bots have been a security headache for more than a decade. What changed is that the most valuable visitor to a site is now also a bot: the retrieval and training crawlers that feed large language models. “The VIP visitor to the website are the various bots that really make LLMs go,” he says. Detection and blocking are no longer the point. Akamai keeps dozens of categories of bots and a menu of responses for each.

For Salem-Jackson, the reframe is sharper. She treats LLMs as a new audience, each with its own personality, the way a marketer treats different buyers. “The bots are my new customer,” she says. She monitors bot volume by the hour, rolls out a welcome mat, and watches whether her team’s work on AI visibility pulls more crawlers in.

The catch is that the same bot looks like an opportunity to one business and a threat to another. A training crawler can compress a publisher’s entire site, hand it to a model, and never send a reader back. Sullivan puts the ratio of training visits to human visits at tens of thousands to one. For a company like Akamai that wants to show up in AI answers, that crawler gets cookies and milk. For a publisher whose business runs on traffic, the same crawler is something to block or charge for.

That’s where the conversation gets useful for media people. Akamai offers pay-per-click tools, but Sullivan is candid that those models are early and that content licensing deals remain more common. The technology is the easy part. The hard part is the business and legal call about what to allow, and who can enforce it. Unlike Cloudflare, which has taken a loud public stance on publisher control, Akamai casts itself as “Switzerland,” a technology enabler that leaves the decision to the publisher.

Then there’s the part every publisher can act on now. Salem-Jackson says an average webpage runs about 200,000 tokens, but a model reads only around 10,000. Roughly 1% of your site actually gets consumed. Anything important that sits below that budget is invisible. Her fix was a separate “bot site” that serves crawlers stripped-down, high-value content, which she credits for an 85% lift in citations. Great content is not enough if the machine cannot read it.

Key takeaways

 AI bot traffic is up more than 300% a year, and bots now make up about half of all web traffic

• For some sites, the most valuable visitor may now a bot, not a person

• The difference between training bots, retrieval bots, and malicious bots

• Marketers are starting to treat LLMs as an audience to court, each with its own personality

• The same crawler can be an asset or a threat depending on whether your business runs on visibility or on traffic

• Pay-per-click for bots exists, but licensing deals are still the more common path, and enforcement rests with publishers rather than the CDN

• A model reads only about 1% of a webpage, so anything below the token budget is invisible to AI

• A separate bot-optimized site can raise AI citations sharply, which Akamai puts at 85%

• Wikipedia, LinkedIn, YouTube and Reddit carry outsized weight in what LLMs cite

🔗 About the 👤 Guests

Kim Salem Jackson
LinkedIn: https://www.linkedin.com/in/kimsalemjackson/

Patrick Sullivan
LinkedIn: https://www.linkedin.com/in/patricksully 

Akamai Technologies
https://www.akamai.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: Hi, welcome to the Media Copilot. It’s a podcast about how AI is changing media, news, and communication. My name’s Pete Paschel, and 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 do we get information in the future? And how will that change the jobs and the industries whose business is information, especially media?

Kim: Okay.

Pete Pachal: For most of the web’s history, there was a fairly straightforward exchange. Search engines indexed information, sent people to websites, and those visits helped support advertising, subscriptions, and the creation of more information. AI is changing that exchange. Bots and agents can now read a site, summarize its work, recommend its products, answer questions based on its information, and sometimes even act on a user’s behalf. The original source may receive a citation, a smaller number of visitors, or really no measurable benefit at all. That puts companies such as Akamai in a powerful position. The infrastructure sitting between a website and the rest of the internet can identify automated visitors, can decide what gets through, enforce access rules, and potentially create new systems for licensing and payment. Akamai says it processes more than 150 billion bot requests each day and has recorded more than a 300% annual increase in AI bot traffic. It is developing tools that let companies block, permit, or verify, or monetize AI access while also helping brands improve how they appear in AI generated answers. My guests today work on opposite but increasingly connected sides of that problem. Kim Salem Jackson is Akamai’s executive vice president and chief marketing officer. She leads the company’s global marketing operation and has overseen its work on measuring and improving brand visibility inside AI platforms. Patrick Sullivan is Akamai’s vice president and CTO of Security Strategy. His work includes bot management, how to identify agents, edge security, and the systems that could allow content owners to control or charge for automated access. Kim, Patrick, welcome to the Media Copilot.

Kim: Thanks for having us, Pete. We’re thrilled to be here.

Pete Pachal: Nice. Good to see you both. All right. So before we get into all the tech, I would love to understand your both your two areas a little bit line a little better. I know I summarized them just there. but it I want to understand them and also how they converge around the problems I outlined. So Kim, maybe why don’t we start with you and you can begin from the marketing side and then Patrick, why don’t you take over and explain how it looks from a security perspective.

Kim: Sure. Well, you know, as a CMO I think about two main things. I think about our our brand and awareness and I think about driving demand and revenue for sales. And obviously AI has completely changed the paradigm on both sides of the coin about what I get up every morning thinking about. And the partnership between the business and IT, and in this case working with Patrick, has never been stronger, right? As AI becomes and AI bots become one of our most important customers and our VRP visitors. So that’s a little bit about Macro my role and how I think about the whole world. changing.

Pete Pachal: Patrick, how’s it look from your end?

Patrick Sullivan: Yeah, absolutely. So so I, you know, I think on the security side, the application security team has been handed the challenge of managing bots for more than a decade. you know, that’s an area that you know, I’ve been working with some of the world’s largest brands to help deal with you know, some of the threats, including sort of the relentless testing of compromised credentials on websites. That’s been sort of the number one way websites get breached for you know, most years out of the last decade. so the the security team was dealt that challenge of, you know, can you identify the bots, categorize the bots, optimize the way that you respond to the bots, and then provide monitoring and and analytics. so that, you know, we continue to see fraudsters, you know, leveraging bots, but as Kim said, on the other end of the spectrum, Now the VIP visitor to the website are the various bots that that really make LMs go, right? The retrieval bots, the training bots, et cetera. so that is now the the most important visitor.

Pete Pachal: So it sounds like from your perspective, the change isn’t just volume. It’s that the nature of the bots is different. And whereas before I imagine it wasn’t un universal, but generally like if you saw a bot, it was probably not doing good things. And then and now it’s like, well, half the internet’s bot traffic because everyone’s using AI. So it’s a sort of a difference in kind as well as a difference in volume.

Kim: And you as a marketer, I always kept an eye on the bots. Obviously, we’re Akamai and we use our own technology, but now I’m obsessed with the bots and I want to understand good bots versus bad of the good bots, where are they coming from? As Patrick said, what are they doing? You know, how many can I let in without my security team getting nervous that we’re, you know. letting in the bad bots. So it’s interesting how even as a marketer, my obsession has changed and, you know, I’m monitoring it all the time. Whereas before I’d probably look at it, you know, every six months just just to kind of understand the ebb and flow of them.

Pete Pachal: So imagine there must be like good bots, bad bots, and sort of gray bots, you know? Like I how do you classify these? This is probably more your area, Patrick, but I mean like I guess in terms of how you’re applying the filter. And then Kim, how are you interpreting what’s getting through that filter? Why don’t we start with you, Patrick?

Patrick Sullivan: Yeah, Pete, you nailed it. I mean, there’s a whole kaleidoscope of of bots, right? You know, starting with classically the the the sacrosanct bot that you don’t want to mess with is the Google bot and Bing Bot. You know, you wanted to make sure that that you showed up on search, you know, historically. then you would have some partner bots, you move your way down the spectrum. Maybe there are some aggregators and scrapers that you know, it’s it’s okay if they’re coming through as long as they don’t start to become excessive or cause issues to the websites. And then, you know, you move all the way over to the more parasitic bots that are fraudsters and competitors scraping your website. So there’s always been this continuum. So we’ve always thought about detect the bots, which is not trivial, categorize the bots. You know, we have dozens of categories for the bots and it’s not about blocking the bots, it’s about having you know, a whole menu of responses. And then also you want to make sure that you have your monitoring and analytics so you understand the implications of what you’re doing. But I would say things have radically changed as I’m sure Kim’s about to tell you, you know, that optimize verb, we’ve added some things around making sure that the AI bots in particular get exactly what they want. And there’s a feedback loop there with the monitoring and analytics.

Pete Pachal: Well, come back to that thought about you know, th not just blocking. I think you it’s it’s can be blocking, not just not always. I I think is what you meant. So there’s more subtlety there. But Kim, like obviously you’re thinking this not just in terms of bots, but like audience, right? And who who are these bots representing? What is as as sort of these bots get through to sites, like how do you how do you interpret that?

Kim: Yeah, I mean, Pete, you nailed it. To me, the bots are my new customer, right? I think of the LLMs as a new target audience for us, which marketing has done since the beginning of time. And then, you know, I’m also thinking about, okay. Each bot has a or each LLM has a different personality, just like each buyer in marketing has a different personality, business or IT. So my team is not only working with Patrick and understanding what the bots are, but you know, where they’re coming from, what they’re scraping, what they’re doing, and how I can more precisely market to them to align to their needs. So I literally think of them as my new target audience and I’m trying to understand their behavior, retrieval. training. I like the retrieval ones because I’m hoping they’re delivering information about Akamai that’s a signal to buying. That’s my favorite behavior. I like to look at the volumes and as we kind of lean into some of our GEO work is that having an immediate correlation to more bots coming to our site if we’re as we’re trying different tactics. So I am constantly monitoring that bots. I have a big welcome sign to let them in and you know I’m sure we’ll get into what we’re doing with our website and The bot site versus the human site, but yeah, I one hundred percent think of them akin to a human. And that the way I would target business buyers versus IT buyers, I just think of those LLMs in exactly the same.

Patrick Sullivan: Yeah.

Pete Pachal: Right. So some people in my audience will definitely understand that the whole idea of the welcome ad and sort of making sure the bot the good bots, I guess as we’re sort of throwing a lot of like judgmental terms here, but like the bots that are having a positive outcome for whatever the business goal of the site is, they would want to well sort of lay out the right carpet and make sure they have a good experience. So that’s a good chunk of my audience or comms marketing, but then there’s another chunk of my audience, which is obviously publishing journalists, editors, and they’re like, Well, wait a minute, like we are

Kim: Come on in. Yeah.

Kim: Exactly.

Pete Pachal: business model is all based on advertising, subscriptions, and other things that depend on traffic. without some kind of compensation system in place, I don’t know if I really even want these bots in place. So let’s k stay with you for a second, Kim, as you as you sort of think about that in terms of like who might be on the other end of whatever Akamai is managing as a CDN and what might be available to them. And and how how that might that like in other words, they might be applying a different filter to to the things you’re talking about.

Kim: Sure, and I’ll I’ll tee it up and then I’ll hand her to Patrick. So obviously we have a breadth of customers and some are like me, you know, that it just wanna put out that welcome at and others need to monetize their site, right? And and really kind of allow that pay per click. And so based on, you know, step one for us at Hakamai is really to understand your business objectives, what role the bots play. in your business model and how best you want to treat them and calibrate it over time. And so you know that’s really where we start every conversation with our customer is understanding their business objectives, their business model and how we can meet them where they are and put the right monitoring and restrictions in place based on that. And Patrick, I know you’ve worked a lot with media customers and been sitting with them. Why don’t you share a little bit more about how we put that structure in place for them?

Pete Pachal: Right. Before before I wanna I definitely want to hear from you next, Patrick, on this and the structure you have in place. But I up until now I think we’ve been sort of talking loosely because we’re just having a chat here about like good and bad bots. But honestly, I loved it if you could get even a little more clinical about it and think about like the different types of bots. So it’s kind of like like the way I think most of my audience and I understand them is like there’s essentially three main types, you know, you have training bots, search bots, agent bots. You might think of it have a different taxonomy in terms of what’s relevant and how you filter, but sort of like as you think about like the different reasons a publisher might want to block or allow through, I think understanding those subtleties would be really helpful to people listening. So please let me know like how the your customers approach this and what you find is the best system that tends to work.

Patrick Sullivan: Yeah, Kim, maybe I’ll take the first crack at this. Yeah. So Pete, there are many, many more categories of bots there, but I think you’re right. We should we should drill in, right? There’s a whole subset that we call the AI bots. And Kim and I both have been talking about training bots, for example. so to to really be specific, what the the role of those bots is on behalf of the LLMs, they’re all visiting websites, you know, ingesting that information and feeding that back into the LLM such that, you know, when an when an agent or somebody interfaces with a chat, they have learned you know from across the web and they’re able to incorporate that knowledge that they derived, you know, from the training bots into the response from the LLM. but but Pete, you nailed that. There is a massive difference in sort of the way people think about those training bots. You know, Kim cannot get enough, right? Like every time a training bot’s coming, you know, she’s you know putting out cookies and and milk, you know, come back, invite your friends. Because you know, because the the really for her, I’m sure she’ll get into the metrics that she tracks, but the more that they come and the more that

Pete Pachal: Literal and virtual cookies, yes, I get it.

Kim: Exactly.

Patrick Sullivan: the LLMs learn about the content on the website, the more that people researching, you know, topics that are relevant, you know, such as, you know, how do we protect ourselves from the latest Frontier LLM security threats or other topics? If those training bots are coming to Akamai and and ingesting our content effectively, we show up for those users. But Pete, to your point, there are other people who really thrive their their whole economics are around intellectual property, And making sure that that’s monetized effectively. And they look at that exact same bot with the exact opposite response. They don’t want that bot coming in for no monetary value, making off with that content and then monetizing it without them, right? So there, you know, the technology is frankly much easier than the legal and business, you know, on this side of the equation. We can do the same thing, we can detect that bot. Categorize that bot. And then when we get to the optimize, rather than the optimize response that Ken will detail in a minute, there, you know, they want to maybe block the bot. Maybe they’ll they want to have a specific API because they’ve structured a financial agreement, you know, where they’re a specific LLM has has structured a deal with a content provider. So rather than crawling, maybe they have a dedicated API. or maybe they’re paying per click. those things are pretty easy for us to instrument from a technology perspective. Honestly, the hard part there are the the lawyers and the business people that have to make those decisions. because it’s pretty easy to say we’re going to block this LLM. The business implications are are far more complex. But yeah, absolutely different people look at the exact same bot from a hundred and eighty degree spectrum. and and we can just help them implement that policy, right? That’s we give them the analytics and then when we get to that optimize, it it is customized to the customer and their business model.

Pete Pachal: So Kim, again, the the the idea of these different kinds of audiences and customers that you have, you know, it seems to be that even within the publishing world, like even if you do have content that is IP invaluable to you, you it’s not a blanket thing typically, right? That you just you want to block everything or protect everything. You might want to like have some content visible and some others. but I know Patrick, you talked about training. Is training really the the main thing right now? I do feel like for a lot of publishers, it’s more about rag these days because they’re constantly publishing content and I don’t know, like so maybe they block training, maybe they don’t, but it’s also about like the stuff that is appearing in people’s answers without the need for them to go to the site anymore. I don’t know if that if the conversation is encompassing all the bots or or if training’s still a part of it, but like I guess what are some of the the subtleties that people can sort of put into this process given the tools that you offer?

Patrick Sullivan: Yeah, mean I I I think it comes back to you know, as as Kim detailed, the the shift in the web, right? you know, it used to be you would go to a search page, click on a link in Google, and then that would navigate you to the web page. These days, if that training bot comes through there and it can you know, compress the information from the website and then incorporate that into the LLM, the the ratio of you know that that training visit to Actual visitors going to the LLM is brutal, right? It could be tens of thousands to one. So one training request comes in, it informs the LLM, and then tens of thousands of of visitors could just, you know, go into their chat interface with their favorite LLM, and there are no subsequent calls back to the website that can be monetized, right? So that the the economics for somebody trying to monetize site visits, can be damaged very, very significantly. And again, for that reason, things like retrieval and and training bots are are treated pretty harshly.

Pete Pachal: Yeah, but I would guess I would I would ask like is is there guidance that you give to publishers on like, well you might want to block training and search but not agents or or all of it? Or is there are there reasons, common reasons you might want to allow one and not the other?

Patrick Sullivan: Yeah, a lot of it it there. I mean, if if your model is strictly about monetizing, you know, I think it comes down to can you strike a content deal? you know, it does blocking some of these bots give you more leverage to to structure that deal. It you know, it’s the technology is is in support of the business arrangement.

Pete Pachal: Well what about like these things where you can play for the usage or the crawls and the sort of like automatic payment or you know, paywalling, I guess, of content from the bot internet? Is there what is what does Akamai offer in that?

Patrick Sullivan: Sure. s so the we do offer, you know, what we call pay-per-click. You know, in that model you would look to monetize as a content provider, you know, every time that that there is a a click within the LLM. I would say in general across the industry, though those models are still you know, emergent. I think they’re still starting to pick up adoption, but very early days there. I I think probably more likely scenario are the the content licensing deals where, you know, again, an LLM would get together with an intellectual property holder and and they would structure a deal for exclusive or or non exclusive use of that content. That’s probably more common than the pay per click model on the internet today.

Pete Pachal: Right. And so you know, I guess it sort of comes down to why, right? Which is to say like I think a lot of AI companies or those that are in the business of I guess you call information brokering, i they there there’s easy ways to get the information even with bot blocking in place, whatever whatever that may be. So you know, it you might be aware that you know Cloudflare’s taking a highly public position on publisher control. presented itself as kind of an advocate for for changing the economics of this. the AI crawling that is. And it’s it’s emphasized giving customers a lot of options. like how how do you how do you how does Akamai sort of see its role? does it have a similar position? Is it a little more is it a different audience, different different philosophical stance? maybe Kim you want to take this one and

Patrick Sullivan: Yeah.

Pete Pachal: And maybe Patrick let me know how it sort of plays out in in the in the technical side.

Kim: Yeah, I mean our our view is again we want to meet our customers where they are based on their business objectives. So you can take a hard and fast stance or or you can adjust. I I was just on a call with a retail customer who views, you know, one of the LLMs as their competitor and they basically wanted to take a really strict stance on that and almost block them entirely. So some of our competitors, you know, take one direction, others are a little more loose, you know, Akamai’s a little more like the Switzerland. We basically again wanna understand your model and help you adjust and and block. Patrick, anything you wanna add? I know you speak to a lot of customers on this top.

Patrick Sullivan: Yeah, Pete, I mean, I would say, you know, if you look at sort of the the delivery engine for the the media industry, you know, Akamai is the top of the list there. on the bot side, you know, we have a a menu of those optimizations that’s broader than anybody. so I would say, you know, there certainly have been some announcements in the industry about changing the fundamental economics of pay-per-click. I I you know, I would ask you to follow up with with some of those folks on the adoption there. but I would say in general across the industry, you know, maybe some of the the announcements have not been followed by quite as much adoption and major shifts to the the funding of the of the internet may not have followed up, you know, with some of the pronouncements that that have been made, but that’s probably for somebody else to to respond to.

Pete Pachal: Well, I guess I guess what I was getting at is that there there’s an enforceability to this that that seems like is relevant, right? Which is to say, like if you’re a major CDN like a Cloudflare or an Akamai, that gives you real influence in the industry, Cloudflare is obviously using that to adopt an advocacy position. And is there is there w I guess would the industry and the things you’re trying to do to help your customers, particularly ones that want a block, would the Would that be helped by either new standards or new maybe even regulatory practices? So for example, what I I know this has been proposed by Tolbitt, who I believe I I’m not sure if you guys are doing things with them. Yeah. So the the in terms of bot identification, that seems to be like, at least from my conversations with them, that’s a good place to start. In other words, requiring that there’s a transparency to what the bots.

Patrick Sullivan: We do. Yeah, we have yeah, yeah.

Pete Pachal: function is, which I think most good players do. But how enforceable is that if players like Cloudflare or Akamai aren’t sort of actively really encouraging that? And I guess I know you both do. It’s just that where where to to what extent, I guess, might be the the the question.

Patrick Sullivan: Yeah. So so I think if we if we follow that through, you know, the the role that Akamai would play in pay-per-click is the technology enabler, right? So if a publisher says we want to move forward with pay-per-click, and the other side of that is, you know, prevent anybody who’s not participating as an AI bot. If they’re not participating in pay-per-click, then we’re gonna block them. they would have to make that decision, right? That that decision rests with the publishers. we’re sort of the enforcement mechanism there, the detection and and technology decision. so it’s really not up to the the bot provider or the CDN to make that decision. That’s sort of the technology enabler. The business owner at a major publisher would have to say, We’re willing to deal with all of the repercussions of blocking LLMs that are not participating in Pay-PerClick. And I think that’s the rub and and sort of maybe what’s driving the current level of adoption of Pay-PerClick. But the the technology is there for publishers that want to adopt it.

Pete Pachal: Okay. So just to probably my last question on this just to say that I think it is like the security framework taking the sort of bot identification, it’s like identifying the agent, like who’s behind like who’s operating it. Like I don’t know if this is there’s an equivalent KYC with with bots or anything. And then just what the purpose it is doing, right? So like that that to me Sounds it would fall on the C DN, that kind of idea. Like just what is the bot and we want to be clear on what it is. So I guess my question would be, is there is there a minimum credential, I guess, for a legit AI agent? And I without getting too technical, what would that be?

Patrick Sullivan: Yeah, so so their AI agent is also a very broad category. So if you think about that, you know, we have you know a whole we published a security framework for you know agentic bots. So we participate in partnerships with major credit card companies, you know, major identified agent platforms. So we we have the ability to understand the trust level of that agent platform, and then we also

Pete Pachal: True, sure.

Patrick Sullivan: Look at the identity behind that, behind that agent. Like, you know, who’s the human identity? And then, you know, there’s various anti-fraud technologies we put in place there. But I guess to bring it back to kind of the framework, we do the detection, we do the categorization. A customer would choose which specific optimizations are right for their business. You know, pay-per-click, block. There are specific optimizations that you want to put in place. if you’re you’re Cam and you want to make sure that you show up on the LLMs. So I think we’ll talk about that in a minute. But there’s a whole menu there and that’s really at the discretion of the the content owner. and publishers respond very, very differently than a commerce website or a travel website or a bank. many organizations now, their number one visitor. the reason you build a website is to attract that AI bot so that you show up and inform the LLM.

Pete Pachal: Mm.

Patrick Sullivan: about the purpose of your business. But it’s very, very flexible, up to the discretion of the the policy of the business. Cool.

Pete Pachal: Cool. Well, let’s switch tracks to that that thing I know Kim’s excited to talk about, which is the visibility in AI answers. And, you know, assuming again you you want to be, that what what is offered and how you guys sort of help enable that. So I was looking the looking that this up, looking this up, and it says, I guess you have an AI brand presence product that it produced an 85% increase in citations. And a 364% increase in non-branded searches, 130-30% increase in child, in other words, up across the board compared with various competitors. So I’m gonna take those those figures as as as as you as you as I saw them. but how how did how do you achieve this? How do you how do you change what do you change in the back end? What are you seeing that other people don’t? What is how does your position as a CDN give you this kind of advantage so that brands can get these kind of results?

Kim: Sure. well there’s kind of two main components to what we look at. One is understanding as you you’ve listed off your visibility within the LLMs, your sentiment, your citations. And then the other one is the action you take to improve it. Right. And that’s where Alchemy Secret Sauce being, you know, a CDN, being in security and being able to deliver that website for the bot comes into play. So gosh, back in the fall of twenty twenty four, I’m losing years Pete, you know, we saw this yeah, we we can’t believe we’ve been doing it this long, we saw the sea change happening in the market and we said, hey, we’ve got to get ahead of

Pete Pachal: Mm-hmm. Yeah.

Pete Pachal: We are.

Kim: This right. And so we started with the visibility journey, which is understanding where we stood vis-a-vis these LLMs on those three KPIs I mentioned. And then the question I always say: data insight action. Okay, now that we understand our standing, what do we do about it? And that’s when we reimagined our org, how we think about content, how we think about content placement. and we started working really, really hard to achieve those stats that you just read off. But what happened in parallel is we were focusing on understanding our visibility, reworking our content engine, reorging marketing, Patrick was working with one of our partners to kind of say, Hey, how can we better serve up information to these bots so they can consume it quicker, more economically? And that’s where it became kind of this perfect marriage of what we were doing and we saw so much success on the Akamai side. And given, you know, who we are as a company, we decided to repackage it and to sell it to all of our customers. So Akamai brand presence gives you those two components. One, helping business leaders and IT leaders, we talked to both, understand their visibility vis-a-vis the LLMs, and then more importantly, give you the ability, it’s I call it more the easier button. to deliver a website specifically for bots. And the second our website for bots went live in November, we achieved incredible results. And you know, those are things the board cares about. I talk to our board about those every single quarter when I do a presentation. So we’re super excited to have to drink our own champagne as I like to say and offer this to to the market.

Pete Pachal: Nice. What are some fundamentals? Like basically what are some basic things either companies, sites, brands aren’t doing or are doing badly or wrong that you’ve found they that they can change, that they can start seeing better results. what’s the I guess what’s the lowest hanging fruit?

Kim: Yeah. Yeah, I mean f first of all, a LLMs consume only one percent of your website, right? So if you make assumption that just because you have great content, build and those LLMs will come, you’re absolutely wrong. If you don’t figure out how to optimize your site to make the consumption quicker and easier, you’re dead in the water. You can have the best content strategy, you can reorg everything, but at the end of the day, it’s about the LLMs being able to quickly consume. your information. And that was really, I think, the secret sauce. So understanding your visibility, which is probably very low if you don’t have a bot site. And then quickly standing up a site for those LLMs so you can get more of your content consumed quickly and at a better economic price.

Pete Pachal: So you mentioned efficiency there, and I understand that like the amount of data that the A systems that they need to process is you you reduce that considerably from when sort of you were you were first studying this. And how do you ensure that like I guess essentially that key th key data hasn’t been lost and it’s still also like consistent with what people see? yeah, how is that is that just technical stuff that d we don’t have to worry about, or is it like Like hi that just seems like an a pretty impress is it just something else only a C DN could do? No.

Kim: well it’s by design. So the average website consumes about 200,000 tokens and AI reads about 10,000. And so, you know, what we did is prioritized our highest value content and make sure that was on our bot site as quick as as possible. And then AI LLMs, they love breadth of of content, they love recency of content, and they like the distribution of consistent content everywhere. And so underpinning having the right tools and the right site. You also have the right, you need to have the right ecosystem under you to make sure you can constantly feed those LLMs in the way in which they want to consume your information. I think the biggest mistake people make is they think you can do one thing or the other, but everything has to work in concert. It’s an entire ecosystem it takes to feed those LLMs, right? From understanding your visibility. serving up the right content in the right places too, because a big aha we had was understanding what sources the LLMs biased. and you know, there were sites I could have cared less about or even Wikipedia I haven’t thought about in 15 years. Now I love Wikipedia because the LLMs love Wikipedia. So I think understanding your stance, understanding the placement of your content, having a content machine, and then again having that brand optimized tool.

Pete Pachal: Yeah.

Kim: to serve up the content via bot site to those LLMs is kind of the secret sauce.

Pete Pachal: Yeah, it does seem like there’s at least kind of a a set of sites. I I see the same four cited all the time as sort of the main things that have outsized influence in LLMs, and that’s like you said, Wikipedia. There’s LinkedIn, there’s YouTube, there’s Reddit. Those seem like the big four now. Is that just kind of a a natural consequence of having essentially user generated, big user generated repositories? Do you think this is something that’s gonna continue? or do you Do you feel like that might get eventually flattened out as the AI web evolves?

Kim: Yeah, I think Pete, it’s all of the above you nailed it. I think the LLMs had an affinity to those sites because they were, you know, raw, structured, plain content, not a lot of text, not a lot of fluff. Now the world understands what the LLMs like, which is why, you know, we launched a site, a bot site for LLMs and we offer it to our customers. But I think the only constant is change in this industry. And so literally every day we’re studying the market. We’re studying the behaviors of the LLMs and we’re trying to ensure that we meet those LLMs where they are with again the right content and the right format in the right place at the right time. And you know, our brand presence tool I think gives people a leg up in achieving that quicker and easier.

Pete Pachal: So yeah, go ahead, Patrick.

Patrick Sullivan: Yeah and yeah, maybe I pick that up. I mean you know, I think to to the point there, the these I think we all know tokenomics, you know, the cost of a token is is driving more and more decisions that are being made. And these large LLMs are certainly aware of their token token burn as they are conducting training or retrieval. So to Kim’s point. The the version of your website that looks beautiful to a human is compelling. It has videos, it has a lot of rich content, and it’s bloated from the LLM perspective. A lot of really important information that you want to show up in that LLM on behalf of your customers is sort of below that that token budget, basically, right? So Kim, you know, was alluded you only get about 10,000 tokens. If really, really important information sits below that. It’s invisible. It does not get ingested to the LLM. We’re seeing customers experience massive business impact because they’re not showing up on the LLMs. Their competitors are, you know, they’re seeing, you know, in commerce and travel, you know, huge revenue shifts to competitors. So what what I found when we started down this path was that. you know, there were IT organizations that were building two versions or more versions of their website, one for the human and another one that that was built maybe for you know a bot that likes very efficient HTML. Yeah, another version it’s worse than that. You know, some like Markdown, most like HTML. You know, so so what we do is just sort of on the fly, we’re making sure that that bots that that we know that like HTML

Pete Pachal: A non human.

Kim: And not

Patrick Sullivan: Or getting HTML that’s within their budget. Those that like markdown get markdown. and then there’s also a feedback loop. You know, we talked about that. You detect the bots, categorize, and then you optimize. The optimize for the AI bots is to give them exactly what they want. And then the feedback loop there is, you know, the the metrics that Kim was mentioning. How are you doing relative to competitors for citations, other metrics? And then there could be content, you know, recommendations, you know. it’s not just making sure that everything, as Kim said, everything that that you have published is showing up, but also maybe there’s some things that you haven’t published about that you should, right? So that’s what we see as kind of the full life cycle.

Pete Pachal: Nice. You mentioned on the fly there, I might understand that like if you’re a a site owner of whatever whatever you’re doing, that you can if if I don’t know what you have to enable on your back end, but like the idea that it it’s identifying a bot and then it’s like spinning up kind of a a very efficient version of that page with the relevant information for the bot right then and there. Is that kind of the idea?

Patrick Sullivan: That’s the model. So as that as a human comes in, they get the human version because we detected that that was a human. as, you know, one of these specific AI bots come in, we’re gonna dial up the optimization and serve them, you know, that optimized version that they want and then capture all the analytics that that Kim thrives on and and is pouring over on an hourly basis of you know, you know, how’s the business doing from a a digital marketing perspective? So so that’s all. you know, part of the the platform.

Pete Pachal: So as a publisher, if you’re a publisher and you have content and presumably want to have it in LLMs, or at least the LLMs that you’ve authorized, the I guess are there any tools that are being underutilized in this regard? Do you do it? Like I I’m I I don’t want to get too nerdy here, but it is like I’ve I’ve recently gone to a couple of talks about snippets and that idea of sort of identifying key content.

Patrick Sullivan: Yeah.

Pete Pachal: whether it is on the pa whether it’s lower on the page or not, and just sort of giving emphasise emphasis to certain aspects of the content for the machine. Is that something that you think about as part of the the overall way you implement things? Curious.

Kim: All day, every day we are we are liter literally updating the site every single day for humans and for bots and always thinking about the highest value content that we want to deliver to either audience. And sometimes it’s slightly different, but it it is a it’s not a you know, turn it on and forget it. It’s a it’s an always on ecosystem that we’re fueling and the measurement aspect is critical because you need to understand how those different things you’re serving up are serving you.

Pete Pachal: Sure.

Kim: vis-a-vis your performance and so that constant tuning is key, but having the ability to have the bot site and the human site makes it that much easier. Before it was very manual, very time consuming. And we saw immediate implications against revenue because more people are going to LLMs to buy, you know, whereas SEO is more about research. When you go into those LLMs, you typically know what you want. You’re looking for that answer. And so this gives you the ability to serve it up quickly and correctly to the LLMs.

Pete Pachal: Nice. As we c start to wrap up here, I’d love to ask a couple questions maybe about standards. And I know, you know, there’s a standard called Real Simple Licensing that has been supported by a lot of groups and companies and publishers, I believe including Akamai and Cloudflare. but basically RSL like it allows publishers to essentially like put the machine readable rules for the bots and and search AI training, etc. I’m I’m curious what you think might still need to happen before something like RSL becomes something meaningful and forceable on the web. Patrick, why don’t you take it?

Patrick Sullivan: Yeah, so so I would say in in general, you know, there are there have always been, you know, pages that provide suggestions to bots, you know, this is the part of the website we want you to crawl, you know, here’s what we want you to do elsewhere. you know, some some bots obey those suggestions, other bots kind of treat treat those suggestions like the speed limit sign on the interstate and you know, maybe they follow it, maybe they don’t, but you know, certainly I think it’s it’s definitely best practice to publish those instructions and there are a lot of bots that will follow those. but there are many, many entities out there, many different parties and some some play closer attention to the rules than others.

Pete Pachal: But I guess what happens is even when the sometim sometimes you can’t even agree on the rules, right? I mean, there was the whole row that ver Cloudflare versus Perplexity last year and perplexity was insisting that it would user agents were just behaving like agents and agents can do what they want or not do what they want, but like you know, they were representing people, so they have sort of a different set of rules than search crawlers or training crawlers. And you know, the subtleties of the we don’t have to rehash the whole thing. But I guess that’s the point of these standards. some extent is like sh do we need new rules as we go forward for for managing this stuff. RSL would seem to be something that that like you say it sort of add gives a set of instructions. like you could you could write theoretically a set of instructions for agents, but they might not do it. And is again i i it do we the I guess it’s push coming back to the enforceability problem of like if someone wants them to, w like the what are the tools that or the things that I guess the levers that s someone like Akamai can sort of pull on to sort of help them.

Patrick Sullivan: Yeah, I I I think it’s there are always, you know, bots that that play by the rules and then there are other particularly when it comes to scraping and things like that, there are always bots that we’ve been dealing with for a decade plus that that do not like to play by the rules and you have to detect them and you know, take enforcement into your own hands. But you know, a lot of the the larger organizations y you know, are much more likely to play by the rules.

Pete Pachal: This has been great, guys. Thanks so much for dropping by. For before you go, I’d love to ask you guys a really quick question. I try to ask most of my guests, which is that is there something about this new AI era and agent driven internet that either keeps you up at night or you’re optimistic about? feel free to tell me both. I always like to get optimism and pessimism about you know what’s gonna happen. but why don’t we start with you, Kim, ladies first?

Kim: Sure, I would say what keeps me up at night is it is unprecedented and changing by the minute. I feel like if you take a day off, you’re behind. so so that would be part one. And what I’m most optimistic about is I think, you know, ultimately we’re all striving for a better customer experience, right? And you know, I don’t think there is a better one than LLMs delivering kind of hyper personalization, real time answers. And so I think as quickly as companies can prepare. I think the end game will be better for everybody.

Pete Pachal: Nice. Patrick, what say you?

Patrick Sullivan: Yeah, so so Pete, I think the the pessimism is pretty easy. You you know, just working in security probably multiple hours every day is just working with large organizations to understand the risk of the latest frontier LLMs as it pertains to finding many, many more vulnerabilities that we had seen before. So it’s it’s a pretty rough year to be in in security with just all the vulnerabilities that that everybody’s facing. so Yeah, definitely take care of your applications. They’re having a rough go. on the optimism side, you know, just looking at at sort of the emerging you know, I think you touched on it, frameworks for for agents, you know, for commerce, for other things. and then also, you know, as it pertains to you know, talking to an organization and saying, you know, we don’t have to publish multiple different versions of the website, you know, manually. We can kind of leverage automation there.

Kim: Yeah.

Patrick Sullivan: That’s a fun conversation to have.

Pete Pachal: Nice. Sounds like you might have had early access to mythos. I’m just guessing here. Akamai. You get to get sir get your hands on that, baby?

Patrick Sullivan: you so we’ve been working with the the you know, many of the latest Frontier L LMs, but every security team out there either had access to them or they’re being asked by the board how to contemplate what are the implications, you know, when the the general public, that’s what everybody’s worried about, when the general public gets access, you know, when all these vulnerabilities come home to Roost. So that’s that that’s definitely the one that keeps us up at night, no doubt about it.

Pete Pachal: Nice. Well, if if no one takes if to if you take nothing else from this podcast listeners, set up your two factor off, among other things. guys, this has been great. Thanks so much for spending some time here on the Media Copilot.

Kim: Yeah.

Kim: Thank you.

The post When bots become the audience  appeared first on The Media Copilot.

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

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

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

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

Sort the bots before you swing the axe

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

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

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

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

Signal value, don’t donate it

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

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

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

Your archive is your alpha

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

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

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

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

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

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

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

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

Turn defense into leverage

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

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

A version of the column appears in Fast Company.

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

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

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

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

Getting into the citation pool early

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

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

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

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

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

The unit of competition has changed

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

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

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

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

Reporting is still the moat

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

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

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

A version of this column appears in Fast Company.

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

By The Copilot & Michele Musso

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

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

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

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

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

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

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

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

In this episode:

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

Why this matters

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

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

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

What happens when suddenly anyone can build almost anything?

About the 👤 Guest

Paul Gewuerz on LinkedIn: Paul Gewuerz

LocalPod website: LocalPod.co

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

About the show:

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

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube? Tap the Like button and Subscribe to the YouTube channel. For more AI tools and resources built for media professionals, visit mediacopilot.ai.

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

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

All rights reserved. © AnyWho Media 2026

Episode Transcript

This transcript has been lightly edited for clarity and readability.

Introduction

Pete Pachal (00:34)

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

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

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

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

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

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

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

Paul Gewuerz (02:46)

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

Pete Pachal (02:49)

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

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

From Audiobooks to Local Journalism

Paul Gewuerz (03:20)

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

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

Pete Pachal (03:58)

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

Paul Gewuerz (04:18)

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

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

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

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

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

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

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

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

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

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

The Untold Stories of Local Media

Pete Pachal (06:15)

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

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

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

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

Paul Gewuerz (07:20)

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

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

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

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

Pete Pachal (08:16)

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

Paul Gewuerz (08:22)

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

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

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

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

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

The Hard Reality of Running Local News

Pete Pachal (10:04)

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

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

Paul Gewuerz (10:36)

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

That’s a reality.

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

Pete Pachal (10:57)

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

Paul Gewuerz (11:05)

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

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

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

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

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

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

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

Why Local Media Must Reinvent Itself

Pete Pachal (12:50)

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

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

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

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

Paul Gewuerz (14:25)

Yes, very much.

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

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

I think the same thing has happened with news.

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

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

Pete Pachal (15:58)

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

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

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

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

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

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

Trust, Community, and New Business Models

Paul Gewuerz (17:30)

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

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

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

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

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

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

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

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

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

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

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

There are cool things like that happening.

Pete Pachal (20:18)

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

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

Paul Gewuerz (20:45)

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

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

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

How Local Newsrooms Are Actually Using AI

Pete Pachal (21:18)

That’s really cool.

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

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

Paul Gewuerz (21:57)

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

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

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

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

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

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

I fundamentally disagree with that.

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

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

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

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

Pete Pachal (24:37)

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

Paul Gewuerz (24:46)

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

Pete Pachal (25:02)

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

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

Paul Gewuerz (25:17)

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

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

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

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

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

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

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

Pete Pachal (26:26)

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

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

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

That feels like progress to me.

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

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

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

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

Paul Gewuerz (28:23)

I’ve felt the exact same way.

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

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

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

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

Could AI Actually Strengthen Local News?

Pete Pachal (29:01)

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

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

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

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

Paul Gewuerz (29:50)

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

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

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

Not everybody has that down.

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

It’s just not the case anymore.

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

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

Because the AI slop out there is ridiculous.

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

Pete Pachal (31:08)

Let’s talk about slop specifically for local media.

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

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

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

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

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

Paul Gewuerz (32:31)

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

I think it’s two different things.

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

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

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

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

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

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

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

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

I think those are the two main culprits.

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

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

Podcasting as a Growth Strategy for Local News

Pete Pachal (34:29)

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

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

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

Paul Gewuerz (35:02)

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

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

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

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

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

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

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

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

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

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

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

What Keeps Paul Up at Night About AI?

Pete Pachal (37:00)

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

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

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

Paul Gewuerz (37:29)

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

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

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

I finished it in the last two months.

It’s operational. People are on the platform.

Pete Pachal (38:00)

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

Paul Gewuerz (38:03)

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

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

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

Pete Pachal (38:18)

Nice. Beautiful.

Paul Gewuerz (38:28)

It’s pretty incredible.

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

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

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

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

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

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

Pete Pachal (39:08)

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

Paul Gewuerz (39:25)

It’s really kind of the same thing.

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

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

Pete Pachal (39:36)

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

Paul Gewuerz (39:39)

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

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

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

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

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

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

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

Things change.

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

Pete Pachal (40:50)

That’s awesome. We’ll leave it there.

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

Paul Gewuerz (40:55)

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

Pete Pachal (41:01)

Cool. We’ll do it again soon.

The post AI didn’t kill Local News. Could it actually save it? appeared first on The Media Copilot.

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

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

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

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

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

The lens publishers can’t remove

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

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

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

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

Three ways the machine gets it wrong

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

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

Building the machine readability pass

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

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

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

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

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

A short set of questions to run through the pass:

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

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

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

A version of this column appears in Fast Company.

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