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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What the click pattern tells you

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

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

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

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

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

Trust converts but not on impact

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

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

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

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

Stop playing defense

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

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

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

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

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

A version of this column appears in Fast Company.

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

This episode is sponsored by: Adobe Acrobat

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

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

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

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

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

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What we cover

• How The Wall Street Journal evaluates new AI technologies

• Why audience needs come before AI innovation

• The rise of personalized and adaptive journalism

• AI tools transforming investigations and newsroom workflows

• How AI can create entirely new reader experiences

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

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

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

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

Why this matters

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

About the 👤 Guest

Taneth Evans Head of Digital, The Wall Street Journal

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

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

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

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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

Pete Pachal (00:25.442)

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

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

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

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

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

Taneth (02:40.285)

Thanks for having me.

Pete Pachal (02:42.35)

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

Taneth (03:07.345)

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

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

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

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

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

Taneth (05:28.765)

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

Pete Pachal (05:34.538)

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

Taneth (05:51.504)

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

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

Pete Pachal (06:45.954)

Mm-hmm.

Taneth (06:46.552)

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

Pete Pachal (06:51.554)

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

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

Taneth (07:24.731)

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

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

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

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

Pete Pachal (09:06.722)

Right.

Pete Pachal (09:18.146)

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

Taneth (09:32.333)

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

Pete Pachal (09:43.534)

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

Taneth (09:49.32)

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

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

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

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

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

Pete Pachal (12:13.708)

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

Taneth (12:27.315)

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

Pete Pachal (12:34.732)

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

Pete Pachal (12:55.01)

Nice. Go on.

Taneth (12:56.487)

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

Pete Pachal (13:02.616)

Cool.

Taneth (13:26.277)

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

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

Pete Pachal (14:18.03)

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

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

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

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

Taneth (15:48.964)

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

Pete Pachal (16:02.988)

Ha ha ha.

Taneth (16:18.195)

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

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

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

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

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

Taneth (18:38.373)

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

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

Pete Pachal (19:17.218)

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

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

Taneth (20:11.405)

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

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

Pete Pachal (20:46.476)

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

Taneth (20:51.315)

No, time the concept, I’m sorry.

Pete Pachal (20:58.252)

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

Taneth (21:01.171)

you

Pete Pachal (21:24.588)

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

Taneth (21:34.558)

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

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

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

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

Pete Pachal (23:25.708)

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

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

Taneth (24:07.513)

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

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

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

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

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

Pete Pachal (26:05.516)

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

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

Taneth (26:40.023)

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

Pete Pachal (26:56.195)

Mm-hmm.

Taneth (27:07.703)

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

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

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

Pete Pachal (28:13.036)

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

Taneth (28:36.369)

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

Pete Pachal (28:40.386)

Hmm.

Taneth (29:02.503)

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

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

Pete Pachal (29:32.334)

Mm-hmm.

Pete Pachal (29:55.021)

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

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

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

Taneth (31:17.244)

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

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

Pete Pachal (31:29.39)

Mm-hmm.

Taneth (31:54.897)

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

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

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

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

Pete Pachal (33:38.467)

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

Taneth (34:02.076)

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

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

Pete Pachal (34:34.904)

Right.

Pete Pachal (34:53.794)

No, no.

Pete Pachal (34:58.552)

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

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

Taneth (35:57.341)

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

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

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

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

Pete Pachal (37:18.146)

Nice.

Taneth (37:26.74)

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

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

Pete Pachal (38:06.636)

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

Taneth (38:34.93)

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

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

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

Pete Pachal (39:33.55)

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

Taneth (39:52.338)

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

Pete Pachal (40:05.902)

Mm.

Taneth (40:22.024)

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

Pete Pachal (40:41.144)

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

Taneth (40:50.716)

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

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

Pete Pachal (41:31.82)

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

Taneth (41:35.912)

Thank you so much.

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