A translation of a mass shooter’s journal written in Faux Cyrillic. A scraper that pulled every public meeting minute from a Texas county website. Tens of thousands of leaked emails from a Chinese surveillance firm, made searchable with a large language model.

Each of those reporting efforts won or was a finalist for a Pulitzer Prize this year. And each relied, in some way, on AI.

Nieman Lab’s Andrew Deck reports that eight Pulitzer honorees disclosed AI use to the judging committee in 2026: five winners and three finalists. That’s the most since the Pulitzer Prizes began requiring AI disclosures in 2024—and it points to a shift in how journalists are using the technology.

In the two years before that, reporters mostly used older forms of machine learning — embedding models for data visualization, for example, or pattern-recognition tools to analyze satellite imagery. This year, commercial large language models did more of the heavy lifting, especially when reporters faced mountains of documents.

The approach was similar across this year’s Pulitzer winners. At The Wall Street Journal, computational journalist John West and his colleagues built a custom scraper to collect records from Kerr County, Texas, after deadly summer floods. They then used an internal tool called WSJPT to summarize every page and flag references to previous flooding events. Reporters still read each flagged section themselves.

“We aren’t obviating the need for human investigation of a pile of documents,” West said. “Instead, we’re trying to sort the pile so the most relevant stuff is right at the top.”

At The Minnesota Star Tribune, engineer Dana Chiueh used an enterprise ChatGPT account to analyze screenshots of a Minneapolis church shooter’s journal. The model identified the writing as Faux Cyrillic and produced a first-pass translation of more than 600,000 words.

The team then used Google’s NotebookLM to identify recurring themes and had two Russian-language academics at St. Olaf College verify key passages. Their review caught several errors, including one that mischaracterized the shooter’s motivation.

The reporting won the Pulitzer Prize for Breaking News.

The Associated Press used large language models to make tens of thousands of leaked documents searchable for its Pulitzer-winning investigation into American technology companies’ role in China’s surveillance state.

But reporters did not simply trust what the models surfaced. They manually reviewed documents flagged by AI, independently checked the accuracy of AI-generated summaries and did not quote from those summaries, said AP investigative journalist Garance Burke.

The New York Times flipped the usual workflow. For its investigation into the Securities and Exchange Commission’s retreat from crypto enforcement under the second Trump administration, reporters manually read and classified more than 10,000 documents. They then used OpenAI’s GPT-5 to conduct a second pass, flagging discrepancies for reporters to review.

In this case, AI was used to check the humans — not the other way around.

For newsrooms, perhaps the more revealing detail is what readers never saw.

The Wall Street Journal did not disclose its use of AI in the flood investigation at publication. West said the tools functioned essentially as a more sophisticated search system.

That gap between disclosing AI use to a prize committee and disclosing it to the public is at the center of an increasingly important debate over newsroom standards. It’s a tension that has surfaced repeatedly as news organizations figure out when AI use is significant enough to tell readers about.

Pulitzer administrator Marjorie Miller said the industry now has a clearer understanding of where AI can be used appropriately, such as data collection and analysis, and where its use raises more questions, including writing and editing.

Next year, after controversies over AI-generated text in prize-winning literary works, the Pulitzers will add an AI disclosure question to their book entry forms.

“AI is here to stay,” Miller said. The committee, she added, will continue asking entrants to demonstrate that a human produced the work.

The post Eight Pulitzer awardees disclosed AI use this year, a new record appeared first on The Media Copilot.

Anthropic put Claude Opus 5 on sale today at $5 per million input tokens and $25 per million output tokens, the exact prices it charged for the previous Opus 4.8. According to Anthropic’s announcement, the model reaches close to the intelligence of its higher-end Fable 5 model at half the cost, and it now serves as the default model on Claude Max and the strongest option on Claude Pro.

The company is leaning hard on benchmark numbers to make its case. On Frontier-Bench v0.1, Anthropic says Opus 5 beats every other model and more than doubles Opus 4.8’s score at a lower cost per task. On ARC-AGI, a test built around novel reasoning problems, the company reports Opus 5 scoring three times higher than the next-best model. On Zapier’s AutomationBench, which checks whether a model can run a business task end to end, Anthropic claims a pass rate roughly 1.5 times the nearest competitor at the same cost.

The recurring theme in the launch is verification. Anthropic describes Opus 5 checking its own work before handing it back. In one Frontier-Bench task, the model was asked to rebuild a machine part in 3D code but given no way to view the drawing, so it wrote its own computer vision pipeline to pull the geometry from raw pixels. In another example, it found the root cause of a bug in an open-source package manager that the community’s own patch had missed.

Early-access customers echoed that framing. JetBrains said the model catches its own logical faults during planning rather than after. A legal-tech tester reported first-turn redline scores nearly double Opus 4.8. Box measured an 8% overall improvement over Opus 4.8, with 17% gains on due-diligence workflows. These are vendor-supplied quotes, so treat the precise figures as marketing rather than independent measurement.

On safety, Anthropic says Opus 5 is its most aligned model so far, scoring 2.3 on its automated misaligned-behavior audit, the lowest of its recent releases. The company also notes the model stays behind its Mythos 5 model on biology research and offensive cybersecurity. Notably, Opus 5 can find software vulnerabilities about as well as Mythos 5 but lags badly at writing exploits for them, which Anthropic frames as a deliberate safeguard. Its cyber classifiers block binary vulnerability scanning, penetration testing and exploit generation, with flagged requests falling back to Opus 4.8.

For newsrooms and publishers, the pricing is the story. Holding costs flat while claiming stronger reasoning and cleaner outputs matters for teams running document analysis, research summaries and data work at volume. The customer notes about tighter, more concise responses and fewer tool calls point to lower token spend per task, which is where AI budgets actually get decided. Publishers weighing model choices should still run their own tests against real workflows rather than trusting benchmark charts, a point we’ve made repeatedly at The Media Copilot.

Anthropic paired the launch with two beta features: mid-conversation tool changes that don’t break the prompt cache, and automatic API fallbacks that route flagged requests to another model instead of blocking them. A Fast mode runs about 2.5 times the default speed at twice the base price. The bet is that a cheaper, more careful default beats a smarter but pricier one for daily use, and the next few months of real deployments will show whether that holds.

The post Anthropic ships Claude Opus 5, pitching frontier work at half the price appeared first on The Media Copilot.

The Associated Press will let its journalists use AI to draft headlines, summarize documents and handle transcription and translation, according to updated newsroom standards the wire service released this week. What it won’t do is hand over the parts of the job that carry legal and reputational weight.

The guidance draws a firm line: AI can assist with specific tasks, but reporting, sourcing, editorial judgment and verification remain the responsibility of human staff. Every AI-generated output must be reviewed and edited by an AP journalist before anything reaches readers.

The approved uses of AI are limited to a narrow set of practical tasks. AI can support early-stage research and document summarization, help with transcription and translation, suggest headlines, story summaries and shot lists, and clean up grammar, spelling and search optimization. AP kept one prohibition intact — generative AI cannot be used to create, alter or enhance news photography.

The updated standards underscore AP’s hard line on image authenticity as AI-generated visuals become more widespread. The news organization has long classified photo manipulation as a fireable offense, and the new guidance applies the same standard to AI-generated imagery.

The update also expands the guidance in several areas. It establishes newsroom standards for verifying and reporting on AI-generated and manipulated content, requiring journalists to clearly identify and contextualize such material when it appears in AP coverage. It also introduces disclosure requirements when generative AI materially contributes to published work. Beyond the newsroom, the policy adds guidance for AI coding assistants used in software development, reflecting the technology’s growing role across the organization.

The disclosure requirement may prove to be the policy’s biggest test. While AP requires disclosure when generative AI materially contributes to published content, it does not specify what qualifies as a “material” role, leaving room for editorial judgment.

AP’s approach offers a benchmark for other publishers developing AI policies. The standards permit AI to assist with newsroom workflows while reserving editorial judgment for journalists.

The updated standards are likely to be closely watched by other news organizations refining their own AI policies. The organization treats AI as workflow assistance under human review, not as a byline replacement, and it pairs that with disclosure and content-labeling rules. These guidelines also land as regulators and labor groups push for formal rules, including the New York AI transparency effort backed by major unions that would require newsrooms to disclose AI use.

As more publishers move from experimenting with AI to formalizing newsroom rules, AP’s updated standards offer one of the clearest examples yet of where a major news organization is drawing the line: AI can assist the reporting process, but accountability for what gets published remains with journalists.

The post AP doubles down on human oversight in updated AI newsroom rules 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.

More than half of newsroom leaders surveyed for a new global study said staff skepticism is the single biggest thing holding back AI integration, as reported in Press Gazette.

The Future Newsrooms Study 2026, from FT Strategies and WAN-IFRA, collected responses from 448 people across 86 countries, most of them editors-in-chief or executive leaders. It found 52% named “cultural resistance or skepticism” as the biggest barrier to wider AI adoption, and 61% blamed a lack of internal technical skills or expertise.

Another 45% pointed to missing strategic direction. Putting the numbers together reveals a pattern: newsrooms aren’t sure what they’re using AI for, staff don’t trust it and nobody’s teaching them how to use it well.

Six in ten newsrooms offer no formal AI training at all. Where training does exist, the report calls it “generic” and “not specific to journalistic needs.”

The study’s central recommendation is structural. Newsrooms that embed an AI authority inside the editorial team, rather than setting strategy from outside, see higher adoption and more confidence among staff. Yet 57% of respondents had no AI expert in the newsroom at all. As the report puts it, having someone “directly within the newsroom to advocate for AI in the editorial context matters to adoption rates.”

Confidence overall is thin. Just 14% of leaders were very or extremely confident their current tech stack was fit for purpose. One in five had no confidence in it whatsoever.

Lisa MacLeod, director of FT Strategies, framed the findings as a warning. Any newsroom “operating on the old playbook of optimising purely for reach and reactive, breaking news, is actively managing its own decline,” she said.

The report also flags a risk in how newsrooms measure success. A majority (43%) expect AI to cut newsroom headcount over the next three years, while newsrooms default to time savings as the main proof AI is working. That, the authors warn, orients newsrooms toward “doing the same work, just faster and with fewer people” instead of enabling journalism that wasn’t possible before.

For publishers, the takeaway is that the AI problem is a management problem. Buying tools solves nothing if editorial staff aren’t brought into the decisions, trained on journalism-specific tasks, and given someone credible inside the room to answer their questions. The tension between how newsrooms talk about AI and how they actually use it shows how fragile trust remains when tools arrive faster than the culture can absorb them.

AI use in newsrooms remains relatively limited. Most organizations use it for transcription and translation (78%), while only 10% have adopted autonomous AI agents, the highest level of agentic AI use, in any newsroom function. Text remains the main focus, with 97% of those surveyed using AI for written content.

Four years after ChatGPT launched, most newsrooms are still using AI as an assistant, not an agent.

“The data in this report—which will be the first of an annual research effort—provides a stark wake-up call,” MacLeod said. “The truth is that our newsrooms are not well prepared for a disrupted future.”

The post Most newsroom leaders say staff skepticism is holding back AI, survey finds appeared first on The Media Copilot.

beehiiv is expanding beyond email publishing with a suite of new products aimed at keeping independent publishers and news organizations from relying on multiple software vendors to run their business.

At its Summer Release Event on July 16, the newsletter platform introduced four products: Community, a built-in discussion platform; Copilot, an AI agent for audience and business operations; programmatic advertising; and a redesigned visual editor. Together, the launches mark beehiiv’s broadest attempt yet to compete as an all-in-one publishing platform rather than just a newsletter service.

The expansion reflects a shift in digital publishing, where newsletter platforms are increasingly competing to own more of the publisher workflow, from audience engagement and monetization to AI-powered operations.

“We believe the next chapter of the creator economy and content businesses is about consolidation,” co-founder and CEO Tyler Denk said during the event.

For publishers, the most significant announcement may be Copilot, beehiiv’s first native AI product. Rather than serving as a writing assistant, the company describes it as an AI operator capable of analyzing subscriber data, identifying audience segments, drafting marketing campaigns, launching workflows and surfacing revenue opportunities through a chat interface.

The launch builds on beehiiv’s adoption of Model Context Protocol, an open standard introduced by Anthropic that allows AI systems to securely connect with external data and software. As more publishing platforms adopt MCP, AI tools are shifting from generating content to executing operational tasks across newsroom business systems.

beehiiv also introduced Community, a feature designed to let publishers host subscriber discussions inside their own branded websites instead of relying on platforms such as Discord, Slack or Facebook Groups. Paid subscriber communities, moderation tools and podcast integration are built into the feature.

For news organizations, the move reflects a growing emphasis on first-party audience relationships as publishers seek to reduce dependence on social platforms and create additional value for subscribers.

The company also expanded its advertising business with programmatic newsletter ads that automatically match advertisers with newsletters based on audience characteristics and campaign performance. The system is intended to fill unsold inventory alongside direct advertising deals.

beehiiv said publishers on its platform now receive more than $1 million per month through its advertising network and have generated more than $50 million in subscription revenue.

The final launch was a redesigned visual editor that previews how newsletters and website content will appear before publication while supporting email, web pages, automations and recommendations from a single interface.

The announcements highlight how newsletter platforms are evolving into broader publishing infrastructure providers at a time when many news organizations are looking to simplify technology stacks and automate business operations. Rather than stitching together separate tools for newsletters, communities, advertising and audience management, publishers increasingly have the option to consolidate those functions within a single platform.

That consolidation could reduce software costs and technical overhead, particularly for smaller newsrooms and independent journalists. At the same time, it also concentrates more of a publisher’s audience data, monetization and workflow inside a single vendor, raising familiar questions about platform dependence as publishing infrastructure becomes more centralized.

beehiiv also previewed upcoming podcast advertising features, including dynamic ad insertion, signaling that the company intends to expand further into audio publishing as it broadens its reach beyond newsletters.

The post beehiiv expands beyond newsletters With AI and ad tools 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
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• 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.

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

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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.

The tools used to monitor journalists — once confined to intelligence agencies — are now commercially available, widely deployed, and capable of accessing a phone without the target ever clicking a link. On World Press Freedom Day, May 3, the International Federation of Journalists put that reality at the center of its annual assessment of global press conditions, publishing findings that describe not a gradual erosion of media freedom but an accelerating one.

The IFJ, which represents more than 600,000 media professionals across 148 countries, called the global state of press freedom “deplorable.” UNESCO’s latest World Trends Report on Freedom of Expression and Media Development adds the statistical frame: press freedom has fallen 10% since 2012, a decline the IFJ said is comparable to some of the most unstable periods of the 20th century.

128 deaths, and counting

The human cost in 2025 was 128 journalists killed. The IFJ said additional deaths have already been recorded in 2026. Reporters working in conflict zones face the sharpest risks — in Ukraine, Palestine, Lebanon, and Sudan, journalists have been arrested, displaced, or killed while carrying out their work. Individuals identified as press are increasingly becoming deliberate targets rather than incidental casualties.

IFJ General Secretary Anthony Bellanger described each attack as an act with consequences beyond the individual.

“Every attack on a media professional is an attack aimed at silencing a story intended to inform citizens,” Bellanger said, adding that restrictions on journalism ultimately prevent the public from making informed decisions.

Spyware without borders

In a study published April 28 — “Global Surveillance of Journalists: A Technical Mapping of Tools, Tactics and Threats” — the IFJ documented what it describes as a convergence of state intelligence capabilities, private-sector tools, and weak regulatory frameworks.

The report, which draws on cybersecurity expert interviews and technical investigations conducted between 2021 and 2025, identifies commercial spyware systems including Pegasus, Predator, and Graphite as now widely available beyond their original government-intelligence markets. All three are capable of “zero-click” intrusions — accessing a target’s device with no interaction required from the user.

The IFJ found these technologies are frequently deployed with limited oversight, leaving journalists monitored without accountability and with few legal avenues for redress.

AI as force multiplier

The IFJ study also raises concerns about artificial intelligence extending the reach of existing surveillance infrastructure. Data gathered through digital monitoring — communications, location history, online activity — can be fed into AI systems that analyze it at scale. In conflict environments, the report said, such systems can combine telecommunications data with drone feeds, enabling the identification and tracking of journalists in the field.

Beyond targeted surveillance, the IFJ warned of AI-driven disinformation, identity theft, and automated content systems that bypass editorial standards entirely.

Lead study author Samar Al Halal said the effects compound in ways that damage journalism even when no direct harm occurs.

“When journalists are watched, sources disappear, investigations stop, and self-censorship becomes normal,” Al Halal said. “The public doesn’t just lose information, it loses the ability to hold power accountable.”

What the IFJ is demanding

The organization is calling on governments to enact laws protecting press freedom and regulating surveillance technologies, restrict the export and use of commercial spyware, and strengthen legal safeguards for journalists’ sources. The surveillance report specifically recommends increased investment in digital security training and stronger protections for encryption and anonymity.

The broader context makes those demands urgent. A 10% global decline in press freedom over 13 years, 128 journalists dead in a single year, and surveillance tools that require no mistake from their targets — the infrastructure for silencing reporters has rarely been more capable or more available.

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A single unreleased AI model has triggered emergency regulatory mobilization on both sides of the Atlantic. UK financial regulators are holding urgent talks with the government’s cybersecurity agency and major banks to assess risks posed by Anthropic’s Claude Mythos Preview — days after US Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell convened an emergency meeting with Wall Street’s top CEOs over the same concerns.

In the UK, officials from the Bank of England, Financial Conduct Authority, and Treasury are in talks with the National Cyber Security Centre. Representatives from major British banks, insurers, and exchanges are expected to be briefed on cybersecurity risks at a meeting with regulators within the next two weeks, according to Reuters. The BoE, FCA, and NCSC all declined to comment.

The US response was more public. White House national economic adviser Kevin Hassett confirmed on Fox News that Bessent and Powell had convened bank chiefs — including the CEOs of Citigroup, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs — to warn of cyber risks from the model. JPMorgan CEO Jamie Dimon was unable to attend. The urgency of the meeting reflected the capabilities Mythos Preview has demonstrated in controlled testing: the ability to identify and exploit weaknesses across every major operating system and every major web browser.

Anthropic has stopped short of a broad release, citing concerns the model could expose previously unknown cybersecurity vulnerabilities at scale. The company has been navigating an increasingly complex relationship with the broader tech and media ecosystem as its models grow more capable.

What Mythos Preview is — and who can use it

Despite not being publicly available, Claude Mythos Preview is already in active use — under strict controls. Under a program Anthropic calls Project Glasswing, select organizations have been granted access to the model for defensive cybersecurity work. Partners include Amazon, Microsoft, Apple, Google, Nvidia, CrowdStrike, and Palo Alto Networks. Access has since been extended to approximately 40 additional organizations responsible for critical software infrastructure.

Anthropic says Mythos Preview has already found “thousands” of major vulnerabilities in operating systems, web browsers, and other software. The company has committed up to $100 million in usage credits and $4 million in donations to open-source security groups as part of the program.

The framing is defensive. But the same capability that finds vulnerabilities can, by definition, be turned toward exploiting them — which is precisely what regulators appear to be stress-testing.

Why regulators are moving fast

The simultaneous and independent responses from UK and US financial regulators signal that Mythos Preview represents a qualitatively different kind of AI risk than those regulators have previously had to assess. Prior AI regulatory concerns have centered on bias, misinformation, and systemic market risks — as seen in ongoing debates around AI copyright policy and AI use certification. A model with demonstrated offensive capability against critical software infrastructure — in active use, even in a restricted form — is a different category of problem.

It is also a compressed timeline problem. The model exists. It is being used. The regulatory frameworks to manage it are still being assembled.

All three UK agencies — the BoE, FCA, and NCSC — declined to comment on the talks. Anthropic had not responded to a request for comment at the time of the Reuters report.

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The Associated Press, founded in the mid-1800s to help New York newspapers share reporting costs, is offering buyouts to an unspecified number of U.S.-based journalists — the latest move in a long-running transformation from wire service to technology data company.

The News Media Guild, which represents AP journalists, said more than 120 staff members received buyout offers on Monday. AP executive editor and senior vice president Julie Pace said the goal is to reduce global headcount by less than 5%, though she acknowledged the cut among U.S. staff would likely exceed that figure depending on how many people accept.

“We’re not a newspaper company and we haven’t been for quite some time,” Pace said.

The numbers back her up. Over the past four years, AP’s newspaper revenue has fallen 25%. Big newspaper publishers, once the organization’s financial foundation, now account for just 10% of income. Gannett and McClatchy both dropped AP in 2024. Lee Enterprises — publisher of The Buffalo News, the St. Louis Post-Dispatch, and the Richmond Times-Dispatch — is now seeking an early exit from a contract due to expire at the end of 2026.

Where the growth is coming from

While the newspaper business contracts, AP’s technology revenue has grown 200% over the same four-year period. Kristin Heitmann, senior vice president and chief revenue officer, put it plainly: “If you can think of a large technology company, they are a customer of ours.”

AP was among the first news organizations to move aggressively into AI deals, agreeing in 2023 to lease part of its text archive to OpenAI. It has since launched on Snowflake Marketplace to license data directly to enterprises, stood up AP Intelligence to sell data to financial and advertising sectors, and last year secured a deal with Google to deliver news through the Gemini chatbot — Google’s first content deal with a news publisher.

Elections data is another growth vector. AP saw a 30% increase in election data customers between the 2020 and 2024 cycles, and last month agreed to sell U.S. elections data to Kalshi, the world’s largest predictions market. ABC, CBS, NBC, and CNN all signed on to the AP elections service last year.

What the restructuring looks like

Beyond the headcount reduction, AP is doubling down on video — it has already doubled the number of U.S. video journalists since 2022 — and deploying rapid-response teams that contribute to major stories regardless of geographic base. The organization says it will maintain a presence in all 50 states.

The union is pushing back. In a statement, the News Media Guild said AP “refuses to offer [staff] appropriate training and tools” and is “flirting with artificial intelligence — ignoring the opportunity to differentiate AP news stories as ones that are and always will be created by human journalists.” The union also said AP declined a request last week to bargain over AI use.

AP did not immediately comment on either claim.

Pace framed the restructuring as a strategic choice made from stability, not distress. “The AP is not in trouble,” she said. “We’re making these changes from a position of strength.”

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