This episode is sponsored by: Adobe Acrobat

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

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

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

Listen or watch:

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What we cover

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

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

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

Why this matters

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

About the 👤 Guest  

Sharon Goldman on LinkedIn 

Ground Level AI  

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

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

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

All rights reserved. © AnyWho Media 2026

TMC- TRANSCRIPT SHARON GOLDMAN

Pete Pachal (00:19.51)

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

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

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

Sharon Goldman (02:10.748)

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

Pete Pachal (02:14.237)

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

Sharon Goldman (02:30.584)

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

Pete Pachal (02:55.533)

Well timed.

Sharon Goldman (02:58.776)

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

Pete Pachal (03:26.389)

Well those are the days.

Sharon Goldman (03:27.788)

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

Pete Pachal (03:47.266)

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

Sharon Goldman (04:13.196)

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

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

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

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

Pete Pachal (05:48.77)

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

Sharon Goldman (06:19.862)

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

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

Pete Pachal (07:36.344)

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

Sharon Goldman (08:18.86)

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

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

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

Pete Pachal (10:16.257)

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

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

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

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

Sharon Goldman (11:48.706)

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

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

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

Pete Pachal (14:03.703)

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

Sharon Goldman (14:32.994)

Yes.

Sharon Goldman (14:42.87)

Yeah.

Pete Pachal (15:00.917)

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

Sharon Goldman (15:05.26)

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

Pete Pachal (15:21.014)

Right.

Sharon Goldman (15:33.218)

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

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

Pete Pachal (16:12.342)

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

Sharon Goldman (16:34.712)

Right.

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

Pete Pachal (16:43.842)

Yeah.

Pete Pachal (16:50.702)

Mm.

Sharon Goldman (17:06.172)

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

Pete Pachal (17:21.71)

Mm.

Sharon Goldman (17:35.692)

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

Pete Pachal (18:03.982)

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

Sharon Goldman (18:43.458)

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

Pete Pachal (18:46.454)

Okay. Well that’s still the internet.

Sharon Goldman (19:12.576)

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

Pete Pachal (19:33.921)

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

Sharon Goldman (20:05.688)

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

Pete Pachal (20:19.758)

Mm.

Sharon Goldman (20:35.244)

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

Pete Pachal (20:35.288)

Right.

Sharon Goldman (21:01.9)

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

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

Pete Pachal (21:37.507)

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

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

Sharon Goldman (22:40.118)

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

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

Has gotten a lot of traction, I think.

Pete Pachal (24:11.278)

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

Sharon Goldman (24:50.744)

I feel a little

Sharon Goldman (24:55.532)

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

Pete Pachal (25:58.223)

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

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

Sharon Goldman (26:38.04)

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

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

Pete Pachal (27:44.502)

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

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

Sharon Goldman (28:09.634)

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

Pete Pachal (28:21.504)

Right.

Sharon Goldman (28:35.788)

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

Pete Pachal (28:36.013)

Right.

Pete Pachal (28:41.484)

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

Sharon Goldman (28:54.06)

Right.

Sharon Goldman (29:01.1)

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

Pete Pachal (29:12.686)

Mm.

Pete Pachal (29:17.442)

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

Sharon Goldman (29:43.288)

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

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

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

Pete Pachal (31:25.166)

Mm.

Sharon Goldman (31:34.136)

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

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

Pete Pachal (32:08.994)

Sure.

Pete Pachal (32:17.326)

Yeah.

Pete Pachal (32:30.712)

No.

Sharon Goldman (32:32.652)

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

Pete Pachal (32:47.414)

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

Sharon Goldman (33:08.522)

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

Pete Pachal (33:21.197)

Hm.

Sharon Goldman (33:37.677)

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

Pete Pachal (34:01.336)

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

Sharon Goldman (34:15.008)

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

Pete Pachal (34:19.094)

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

of why what you would never use it for.

Sharon Goldman (34:50.264)

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

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

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

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

Pete Pachal (36:28.462)

Mm.

Pete Pachal (36:32.302)

Mm.

Sharon Goldman (36:38.668)

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

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

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

Pete Pachal (37:33.159)

sure.

Sharon Goldman (38:02.476)

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

Pete Pachal (38:12.942)

Sure. Yeah.

Pete Pachal (38:30.509)

Yeah.

Sharon Goldman (38:30.7)

These are the game changers for me as a reporter.

Pete Pachal (38:34.284)

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

Sharon Goldman (38:42.604)

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

Pete Pachal (38:56.856)

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

Sharon Goldman (39:11.552)

Mm.

Pete Pachal (39:25.774)

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

Sharon Goldman (39:36.889)

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

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

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

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

Sharon Goldman (42:02.976)

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

Pete Pachal (42:05.976)

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

Pete Pachal (42:12.683)

Mm.

Pete Pachal (42:26.85)

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

Sharon Goldman (42:52.14)

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

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

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

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

Sharon Goldman (45:16.212)

scope that we can look at for hope.

Pete Pachal (45:19.662)

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

Sharon Goldman (45:33.72)

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

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

The European Commission has published a voluntary Code of Practice on Transparency of AI-Generated Content, giving AI providers and deployers a concrete path to compliance with the AI Act’s labeling requirements—and a clear reason to sign up.

The code, released June 10, 2026, covers two broad categories of obligations. Section 1 targets providers of generative AI systems, requiring them to mark outputs—audio, image, video, and text—in machine-readable formats and ensure their detection as artificially generated or manipulated. The technical solutions must be effective, interoperable, and reliable “as far as technically feasible,” factoring in content type, implementation costs, and the state of the art. Section 2 targets deployers, requiring them to label deepfakes (audio, image, or video that falsely appears authentic) and disclose AI-generated or manipulated text publications on matters of public interest.

The Commission also released a set of standard icons that deployers can use to label AI-generated content. Nicholas Diakopoulos, a professor at Northwestern University, shared them on LinkedIn:

The code is currently under adequacy assessment by the Commission and the AI Board. Once it clears that review, signatories can rely on its measures to demonstrate compliance with Article 50 of the AI Act, reducing administrative burden and gaining legal predictability across all EU member states. Non-signatories will have to demonstrate adequate compliance individually, assessed case-by-case by national market surveillance authorities.

Signatories also gain access to Signatory Taskforces: working groups set up to share implementation practices and advance marking and detection techniques across the value chain.

The code is described as a “consistent, practical and proportionate” implementation framework, not a replacement for the AI Act or the Commission’s forthcoming guidelines on Article 50’s scope.

The code was developed over three drafting rounds between September 2025 and June 2026, led by an independent chair and vice-chair. Participants included AI system providers, detection developers, industry associations, civil society organizations, academic experts, and organizations with expertise in transparency and very large online platforms. International and European observers also contributed without voting rights. Two dedicated working groups handled the providers and deployers tracks separately.

Key milestones included a first drafting round starting November 5, 2025, a second round in January 2026, a third round in March 2026, and a closing plenary on June 10, 2026—the same day the code was published.

The post EU publishes voluntary code on AI content transparency appeared first on The Media Copilot.

This episode is sponsored by: Adobe Acrobat

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

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

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

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

Listen or watch:

• YouTube
• Spotify
• Apple Podcasts

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

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

Sponsor:

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

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

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

Why this matters

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

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

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

What we cover

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

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

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

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

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

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

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

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

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

• How HubSpot balances editorial independence with corporate ownership

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

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

• Why authentic creator partnerships outperform traditional influencer marketing

About the 👤 Guest  

LinkedIn

HubSpot Author Profile

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

Enjoyed this episode?

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

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

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

All rights reserved. © AnyWho Media 2026

The post Tech companies are becoming Media companies. What happens next? appeared first on The Media Copilot.

Look, I’m going to be straight with you. The traditional article is powerful. But it’s only one way to present your reporting.

You spend weeks on an investigation. You publish 3,000 sharp words. But what happens to the data behind it? The full timeline? The quotes that didn’t fit?

What if you could turn that investigation into an interactive experience, complete with clickable timelines, hover-activated charts and tagged insights, in about 20 minutes?

With vibe coding, it’s possible.

What is vibe coding?

The term vibe coding came out of developer culture, but it is no longer just for developers. It’s for anyone who wants to tell a story that harnesses the power of coding.

When you vibe code, you’re building an application with the help of AI by focusing on what you want it to do. Rather than coding with HTML, JavaScript or other technical languages, a builder describes the user experience in plain language to a Large Language Model (or LLM).

You might type a prompt like, “Build me an interactive timeline showing [x, y, z] events.”

Here’s what worked for the journalists I’ve seen succeed with vibe coding:

• Step 1: Pick something simple. Don’t try to rebuild your entire investigation. Start with one article, dataset or interview.
• Step 2: Use a basic prompt structure, like: “Build an interactive [website/dashboard/story] that shows [your content] in [style you want]. Focus on [what matters most].”
• Step 3: After you have something simple, iterate 3-5 times. First pass: structure. Second: visual style. Third: functionality. Fourth: polish.
• Step 4: Share your creation with a colleague. Don’t talk, just watch. See if they click around. If they get stuck, that means you built it for yourself, not your audience. Time to iterate again or start over.

Why vibe coding and journalism make sense

When I taught vibe coding through the Google News Initiative AI Lab, I watched journalists with zero coding experience build interactive financial dashboards, data visualizations and branded microsites, all in about 90 minutes.

“This would have taken our dev team a month,” one person told me. “I did it during our session.”

While you can move quickly to an initial application with vibe coding, you still want to get your product or development support staff on board before launching. The real benefit is that vibe coding lets you prototype faster to see if your idea works before needing to commit resources.

This matters because most newsrooms don’t have a developer on speed dial. At the Adirondack Explorer, a small regional outlet covering New York’s Adirondack Park, journalists are building a civic information product that aggregates town meeting recordings, transcripts and minutes across dozens of municipalities. That kind of project would normally require hiring contractors or a dedicated dev team. Instead, their reporters are building it themselves.

When I worked with VTDigger through the Google News Initiative, they automated campaign emails across four audience segments, work that directly generated $40,000 in donations that likely wouldn’t have happened with manual effort alone. Vibe coding turns “we can’t afford to build that” into “let me show you what I made this morning.”

Think of vibe coding as a creative prototyping partner. Get to 80% quickly. Then decide if you need developer support to get to 100%.

Tips for those who are new to vibe coding

I’m repeating this because it’s important: Start simple. Pick one piece of your big investigation. It could be a dense PDF that needs to be more accessible. It could be data sitting in a spreadsheet. It could even be an interview transcript with great quotes that couldn’t all make it into the article.

When describing what I want to the LLM, I get experimental with my prompts. I’ll type things like, “use the most modern UI and UX interactions and animations to make my charts and graphs more interesting and allow me to parse through the data visually.” Or, “build this in the style of a high-end investigative journalism piece meets Wired magazine’s data viz.”

Then I iterate with edits like, “change the color scheme to match our brand,” or, “pull out more quotes from the sources,” or even, “make [x, y, z] section more prominent.”

Three high-level no-no’s when vibe coding

1. Never use it for final production without verification.
2. Don't use it for anything requiring real-time data without a proper backend.
3. Never publish AI-generated content without independent verification. In high-stakes areas like health, legal, financial or public safety, errors can cause real harm.

A Google AI Overview recently told pancreatic cancer patients to avoid high-fat foods, which is the exact opposite of what oncologists recommend and could jeopardize a patient’s ability to tolerate chemotherapy.

AI can generate beautiful visualizations, but it can also confidently present wrong numbers. For anything where errors could harm your readers, verify everything against primary sources.

Vibe coding tools to try

The main platform I use for vibe coding is Lovable.dev. For a simple interactive graphic such as a timeline, searchable transcript or basic data visualization, you can expect to use roughly 3-8 credits to produce a solid prototype.

More complex builds with multiple views, filtering or light database features can take 15-30 credits depending on how much you iterate. The free tier is typically enough to experiment with small projects, while paid plans make sense if you’re building regularly or refining more advanced applications.

Bolt.new is another tool worth knowing. For a simple interactive project, such as a timeline or basic data visualization, you might use roughly 20,000 to 60,000 tokens depending on how much you iterate. More complex builds with custom logic, multiple components or repeated revisions can exceed 100,000 tokens. The free tier is generally sufficient for small experiments, while larger or ongoing projects may require a paid plan.

Bolt tends to give you more control over the code and works well if you want to edit things directly. Lovable is more beginner-friendly with a cleaner interface for non-technical users.

Both tools let you attach content like article text, CSV files or transcripts, describe what you want in plain language, and get a working prototype you can publish immediately.

You might wonder why you need these tools when you already have ChatGPT or Claude. The difference is output.

When you ask ChatGPT to build you a dashboard, it gives you code snippets you’d need to assemble yourself, often requiring a developer to make sense of it. When you ask Lovable the same thing, you get a working application with a live preview, hosting, and a chat interface to iterate on it.

Lovable is actually powered by Claude, but it wraps the AI in a full-stack builder that handles deployment, databases and design. For journalists without coding experience, that’s the difference between “here’s some code” and “here’s how it looks.”

Go build something new

The article format has served us well. It’s not dead, but it is not the only option.

When thousands of people fly to a conference, share incredible insights and then go home, that knowledge evaporates unless it’s transformed into something people can continue to engage with.

Vibe coding lets us do that better than text-only articles can. Not just for conferences, but for city council meetings, investigative data, community journalism and breaking news.

My hope is you’ll take these vibe coding tips and run. You’ll build interactive story formats your newsroom has never seen. You’ll prototype tools that solve real problems. You’ll make journalism more engaging, more accessible and more honest about its data and sources.

Journalists who can write, build, prototype, ship and transform their own work into new formats will define what news looks like in five years. So, go vibe code something. I can’t wait to see what you build.

This post first appeared in News Media Help Desk.

The post Vibe coding for journalists: Build interactive stories without writing a single line of code appeared first on The Media Copilot.

The Centre Daily Times in State College, PA, has voted to unionize after months of pushback against its parent company’s AI tool—a move that, according to The NewsGuild-CWA, makes it the first newsroom in the union to cite AI adoption concerns as a primary reason for organizing.

As Nieman Lab reported, the Centre Daily Times staff voted to join The NewsGuild of Greater Philadelphia last month. All eligible editorial staff signed authorization cards, and McClatchy voluntarily recognized the union. The catalyst, reporters told Nieman Lab, was McClatchy’s Content Scaling Agent (CSA) tool—an AI system that repackages existing articles into short-form summaries for publication or video scripts—and a March internal meeting where Kathy Vetter, McClatchy’s chief of staff for local news, told staff the company would use their bylines on AI-generated content unless union contracts prohibited it.

Josh Moyer, a senior reporter at the Centre Daily Times, took that as a signal. “It was essentially like, if you’re not in a union, your byline gets used; if you are in a union, we’ll follow what the union says,” Moyer told Nieman Lab. “If we want to control what happens to our byline, that’s the company telling us that we need to form a union.”

McClatchy introduced the CSA tool at the paper earlier this year. Reporters initially published at least one CSA-assisted story per week under a generic byline noting AI assistance. But in late February, management changed the policy: AI-generated content would now carry the reporter’s actual name. Reporters objected that it misrepresented their work to readers.

“When our names go on a thing, it says that this article or video is from that person, but that is just not true in this case,” said Trebor Maitin, a service reporter. Maitin was the first reporter at the paper to have his byline changed to reflect AI assistance.

The NewsGuild-CWA’s president, Jon Schleuss, said unionized newsrooms have had more success keeping AI content clearly labeled: “Unionized newsrooms are the ones where McClatchy’s AI slop gets a clear label. In non-union newsrooms, the AI slop may be carrying a real human reporter’s byline.”

Multiple McClatchy publications have seen byline strikes over the CSA tool, and some have taken labor actions over the tool and related workplace issues. For the Centre Daily Times, the union opens the door to formal collective bargaining and the ability to join coordinated actions at sister publications.

“Some of us use AI a lot more and are okay with it,” Maitin said. “But there is an overall understanding that we need to be able to have a say in this, and that unionizing at least gives us a seat at the table.”

The post A newspaper unionized because McClatchy put reporters’ names on AI content appeared first on The Media Copilot.

Reuters and Time are blocking all AI bots by default and only letting approved crawlers through—a whitelist approach that more publishers are adopting as the volume of unauthorized scraping grows.

As Digiday reports, both publishers moved to block AI bots last month, joining People Inc. and The Atlantic, which adopted similar strategies earlier this year and late last year respectively. The goal is simple: content costs money to produce, and AI companies have been taking it without paying.

“We saw that there was an imbalance between the value that publishers like Reuters provide and the value that Reuters receives in kind, and so instead we went from a default allow-all to a default disallow all,” said Josh London, head of Reuters Professional, which oversees the direct-to-consumer and direct-to-professional businesses. Reuters has since signed AI licensing agreements with Microsoft and Meta, according to the report.

The publishers aren’t relying on any single tool. Reuters uses robots.txt files, a method that is voluntary and non-binding, and one that many AI bots simply ignore. The approach is meant to create friction and signal that access requires negotiation. “If you want this, let’s have a conversation and then we can allow you to access,” said Alphonse Hardel, head of agency at Reuters, who leads the content licensing business.

Time allows roughly 70 bots on its site, ranging from AI lab crawlers and social platforms to its own operational systems. The volume of bot traffic has become significant enough that Time sees it as leverage for a future AI visibility product it’s developing for brand clients.

The economics are also shifting. Blocking bots cuts server costs: Hardel said the expense of the bot-blocking vendor can be nearly offset by the reduction in non-human traffic. At People Inc., the shift from a block list to an allow list meant going from blocking roughly 2,100 user agents to over 30,000, said Lindsay Van Kirk, the company’s SVP of innovation, speaking at an IAB Tech Lab event in May.

“Adding two full seconds of latency to the majority of scrapers when you implement a block-all-bots approach is a really good thing, even if they have to go through,” Van Kirk said. “Every scraper who has to pay a home proxy network in order to get access to the content is margin that you are taking out of their business.”

The IAB Tech Lab has published guidance on bot management, and the SPUR Coalition—a publisher group formed earlier this year with major news organizations—announced significant new membership as it works to create technical standards for AI licensing and content protection.

For Reuters, the change hasn’t reduced site traffic. After monitoring bot activity over an extended period, the company had enough data to identify which bots it could block without hurting revenue. The publisher maintains a public robots.txt file that lists approved bots, a benchmark that also supports enforcement discussions, said Phil Andraos, general manager of Reuters Digital.

“It’s not a set it and forget it approach,” London said. “The value of content is something that we ignore at our own peril, especially as AI scales.”

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A German court has ruled that Google is directly liable for what its AI-generated search overviews say, in a decision that legal observers say could ripple far beyond Germany. As The Decoder reported, the Regional Court of Munich hit Google with a temporary injunction barring it from spreading false claims about two Munich-based publishers through its AI Overviews.

At the center of the ruling is a distinction the court drew sharply: AI Overviews are not search results. They are Google’s own content.

According to the court, Google’s AI Overviews had falsely tied the two publishing companies to scams, subscription traps, and shady business practices for certain search queries. The AI mixed up information about genuinely sketchy companies with the plaintiffs and drew connections that appeared in none of the linked sources. The publishers sent a cease-and-desist letter; Google didn’t respond appropriately, the court found.

The judges classified Google as a direct infringer because the overview “rewrites and judges results in its own words and according to its own structure.” In the case at hand, the AI opened with confident assertions like “Yes, [company] is known for dubious business practices,” then assembled its own summary, red flags, and user tips. Because Google built the AI, offered it, and controls its algorithms, the court ruled, Google owns what it produces.

Crucially, the court found that existing case law shielding search engines doesn’t apply. Germany’s Federal Court of Justice had previously granted traditional search engines limited liability because they merely point to outside websites. But AI Overviews generate “independent, new, and substantive statements,” the Munich court said, and only Google is positioned to check them against the underlying sources.

Google’s defense—that users can check the linked sources themselves and generally know not to blindly trust AI—fell flat. The court ruled that the ability to disprove a statement through further research doesn’t exempt a publisher from liability, drawing a parallel to press law, where outlets are liable for standalone teasers even if readers never click through. The reasoning is bolstered by research showing users almost never click source links in AI Overviews.

The court also weakened free speech protections for AI output, writing that an AI’s opinion is “not the expression of an acquired conviction” but “the result of an algorithm” and largely an expression of Google’s business interests.

Google was ordered to cover 80% of the legal costs, with the plaintiffs paying 10 percent each. The court said the ruling may have international reach.

The decision lands as scrutiny of AI accuracy intensifies. An analysis by AI startup Oumi for The New York Times found Google’s AI Overviews, running the current Gemini 3 model, answered correctly 91% of the time. At Google’s scale, that still means millions of wrong answers every hour—and a legal exposure that could extend to rivals like ChatGPT, Claude, and Perplexity.

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

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

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

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

The infrastructure squeeze is already here

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

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

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

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

Governance is the new AI strategy

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

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

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

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

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

A version of this column appears in Fast Company.

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In today’s competitive economy, nearly every organization aspires to be “data-driven,” but turning that ambition into measurable business outcomes remains inconsistent. Companies widely recognize the value of data and artificial intelligence, yet many are still struggling to operationalize these capabilities at scale.

When data foundations are weak, the effects extend beyond internal operations. Fragmented or unreliable data makes timely, well-informed decisions harder to reach, and increases the likelihood of gaps in areas like security and compliance. Ultimately, those gaps don’t stay internal. They affect the quality and consistency of customer experiences, and the confidence organizations can have in how they’re managing and protecting data responsibly.

Based on new global research from Cloudera, including a study conducted with Harvard Business Review Analytic Services, the gap is stark. Only 7% of enterprises say their data is fully ready for AI, while 27% report their data is not very ready or not at all ready. At the same time, expectations for transformation continue to accelerate, with organizations planning to embed AI across core business functions.

While companies are preparing for large-scale AI-driven transformation, most lack the underlying data infrastructure and maturity required to support it. Until that foundation is in place, the promise of AI remains difficult to fully realize.

Why Data Readiness Is So Difficult

Despite growing investment, data readiness has plateaued.

Enterprise data exists, but it can be hard to find or access because it is fragmented across systems. Over a third (34%) of respondents from the Data Readiness Index survey reported that siloed data was a major issue that prevented them from working together effectively to share, manage, and use data. These silos often stay in place because data isn’t well integrated across systems.

Most respondents said their data sources were somewhat integrated across various environments, but significant gaps remain. Only 30% of IT leaders reported that their data sources were fully integrated, while 52% said they were mostly integrated. While this shows some progress, it also highlights that many organizations are not yet fully prepared to support large-scale AI projects.   

Other barriers compound the problem. IT leaders also identified complicated access (47%), limited data visibility (44%), lack of training (41%), and cultural resistance (34%) as key obstacles.  Each issue slows progress, and together, they create systemic drag. At the same time, regulatory and security pressures are increasing. Data privacy and sovereignty requirements demand tighter control over how and where data is managed. In fragmented environments, meeting those requirements becomes more resource-intensive and more risky.

What “Data Readiness” Means In Practice

Data readiness ultimately comes down to trust and control. Organizations need confidence that their data is accurate, accessible, secure, and governed consistently, regardless of where it resides.

Governance is central to this goal. Findings from the Taming the Complexity of AI Data Readiness survey report show that organizations rank protecting sensitive data and privacy (59%), data quality (46%), and data governance (41%) as the most critical components of their data strategies. These priorities reflect a growing recognition that without strong governance, data cannot be trusted or effectively scaled across the enterprise.

At the same time, the Data Readiness Index reveals persistent structural challenges. Nearly a quarter of organizations (24%) report they cannot access all of their data across environments at any time, and 16% lack complete visibility into where their data resides. These gaps undermine governance at scale, making consistent policy enforcement unreliable and weakening an organization’s ability to manage risk.

Without trust and control, data can’t deliver value. Poor readiness delays insights and decisions as teams struggle to find and trust data. Disconnected environments harm customer experiences by blocking a unified view. Low-quality or poorly governed data leads to missed opportunities and higher risks.

When data is governed and secure, teams move faster and confidently, reducing validation time and increasing value. In the end, organizations must either operationalize data as a strategic asset or absorb the cost of its dysfunction.

A Widening Gap, And A Clear Opportunity

Data readiness is crucial for unlocking AI’s full potential, but readiness goes beyond simply collecting large amounts of data. Organizations also need systems that make trustworthy data connected and usable across the business. That includes improving data quality, establishing clearer governance and access controls, and creating visibility into where data comes from and how it moves through different systems.

These foundational efforts may happen behind the scenes, but they ultimately shape how effectively organizations can apply AI in the real world. In practice, the companies most likely to pull ahead may not be the ones adopting AI the fastest, but the ones building systems capable of delivering reliable, scalable outcomes over time.

This story was produced by Cloudera and reviewed and distributed by Stacker.

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New research from more than 2,500 enterprise leaders finds the chatbot handling your support request has a better-than-even chance of having already been taken offline and restarted.

The AI-powered chatbot failed. The customer repeated themselves three times, got a confidently wrong answer, and gave up. For the company on the other end, that interaction didn’t just cost a support ticket but something harder to win back.

That scenario is playing out at scale. A new survey of 2,527 enterprise decision-makers across 10 countries conducted on May 12 finds that 74% of companies that deployed AI agents in customer communications have been forced to shut them down or roll them back, often after customers had already experienced the failure firsthand.

The research, published in May by communications infrastructure platform Sinch, examines a specific and underreported problem in the AI market: not whether companies can deploy AI in customer communications, but what happens after they do.

AI customer service has gone mainstream

There’s a widely accepted story in enterprise AI that the biggest challenge is getting pilots into production. McKinsey reported in 2025 that two-thirds of organizations remained stuck in experimentation phases. BCG found that 60% had yet to show any material value from their AI investments. Gartner forecast that half of all generative AI projects would be abandoned after proof of concept.

In customer communications specifically, something different happened. A Sinch study shows 62% of organizations already have AI agents live in production across customer channels, and 88% expect to be there by the end of 2026. That’s nearly 9 in 10 businesses actively deploying AI agents by the end of this year.

Enterprises aren’t dipping a toe in either. The average deployment spans 3.3 channels simultaneously, with nearly half running AI across four or more, including web chatbots, email, social media, WhatsApp, SMS/MMS, RCS, and voice and interactive voice responses. And the goal driving most of that investment isn’t cost reduction. For 36% of respondents, the primary objective is improving customer satisfaction and loyalty. They’re using AI to compete on customer experience, and to earn something harder to measure than efficiency: customer trust.

By every metric the market established to measure AI progress, these organizations won. They escaped pilot purgatory. They crossed the finish line.

Except that wasn’t the finish line.

Going live turned out to be the easy part

Here’s the finding that should make every AI communications leader stop and read more carefully: Research by Sinch from 2026 shows that 74% of organizations have been forced to shut down or roll back a live AI customer communications agent.

That holds across every region and every industry in the study. It doesn’t decline with experience. It doesn’t decline with investment. All along, the market has been drawing the wrong finish line, and what happens after enterprises successfully ship radically changes the question every AI communications leader should be asking right now.

More oversight hasn’t stopped the shutdowns

Here’s where it gets interesting. Among organizations that describe their guardrails as fully mature, the most governed, most monitored AI programs in the survey, the rollback rate is 81%.

More governance, more monitoring, more investment, and still 8 in 10 of the most advanced programs have had to shut something down.

The data offers a worrying explanation. Organizations with mature governance instrumentation can see failures that less mature organizations miss entirely. The programs reporting lower rollback rates aren’t necessarily running cleaner AI, and in many cases, they simply lack the monitoring to know when something goes wrong. The organizations reporting no governance failures are not the benchmark. They may just be the ones with the least visibility into what’s happening.

And then there’s the confidence problem: 90% of enterprise decision-makers describe themselves as confident in their AI agent readiness. Of those already in production, 75% have experienced at least one governance rollback. Confidence doesn’t correlate with fewer failures. In many cases, it’s the precise condition under which the next failure is being prepared.

The more useful question for any leadership team is, “If something went wrong right now, would we know before our customers did?”

When the chatbot goes down, brands feel it in three ways

When an AI communications agent fails in production, customers notice. The research shows the impact splits in three directions simultaneously, and most organizations are only tracking the first.

Research by Sinch from 2026 shows an increase in the support queue (35%) and reputational damage to the brand (34%) are the biggest impacts of AI agent failure.

Why support wait time spikes

Thirty-five percent of organizations cite a surge in human support agent load as the primary consequence. The agent goes down, and every interaction it was handling reverts to a human. A support team sized for a world where AI handles significant volume is suddenly managing all of it. At peak moments, a product launch, a service outage, a seasonal spike, that’s not an inconvenience. It’s an operational crisis.

This is the failure mode that gets reported upward. It shows up in dashboards, generates incident reviews, and resolves when the agent comes back online. It’s visible, it’s measurable, and it has a clear path to resolution.

Why the brand damage outlasts the outage

Thirty-four percent cite reputational damage and loss of customer trust, essentially tied with support overload. That near-tie is one of the most underreported findings in the survey, because these two failure modes don’t resolve the same way. The support queue clears. Brand damage doesn’t have a clear path back.

From the customer’s perspective, there’s no platform, no vendor, no infrastructure layer. There’s only the company’s brand. For 31% of organizations, the leading cause of a governance failure rollback is customer data exposure: personal information surfacing in an interaction where it shouldn’t have. That attribution is permanent in a way that a queue spike is not.

What makes this harder to address is that it often isn’t visible to the people who could act on it. Technical leaders report rollbacks at a higher rate than their business counterparts at the same organizations, 77% versus 69%. In retail, C-suite executives are 2.3 times more likely than their VPs and directors to say most AI communications pilots are succeeding. Same organization, very different accounts of the same events. That visibility gap is where the brand takes the hit.

The hidden engineering cost behind every AI launch

There’s a third cost that appears in neither the dashboard nor the customer complaint. Sinch data shows 84% of AI communications engineering teams spend at least half their time building guardrails and safety controls, instead of building the next customer experience. Thirty-five percent spend most of their time there.

And the direction of that burden surprises people. Production-stage engineering teams are spending more time on safety infrastructure than pre-production teams, not less. Each new agent, each new channel, each new compliance requirement adds another layer. The guardrail tax doesn’t amortize. It compounds.

“Every team needs to decide what controls belong at the platform layer and what their engineers should build on top, because the cost of building custom guardrails compounds over time, especially as the team moves through the product lifecycle,” says Anton Efimenko, SVP software engineering at Sinch. “Each new agent, each new channel, each new deployment adds to the pile. And eventually you lose that momentum when it comes to outperforming on the market.”

The real problem runs deeper than the AI itself

Across every statistical method applied to this dataset—correlations, regression models, cross-tabulations—one variable consistently outperforms all others as a predictor of AI deployment success: communications infrastructure satisfaction.

It’s not investment level, AI maturity, how long you’ve been in production, or how sophisticated your safety policies are.

The correlation between infrastructure satisfaction and AI deployment confidence is 0.52, the strongest relationship across 4,656 variable pairs analyzed in the study. How an organization feels about its communications infrastructure is a better predictor of AI success than anything else measured in the study.

Yet most organizations identify at least one significant shortcoming in their current provider. The most common gaps: insufficient reliability for AI at scale (42%), limited multi-channel capability (37%), and lack of AI platform integrations (32%).

And more than half of enterprises (55%) are custom-engineering the ability to preserve customer context when someone moves from one channel to another, from chat to voice, from WhatsApp to a phone call, because their platform doesn’t provide it natively. When a customer has to repeat themselves to an AI agent, they’re not experiencing a model failure. They’re experiencing the infrastructure gap directly. And it’s the company’s brand that pays the price.

The industry has voted with its budgets, trust, security, and compliance is the number one spending category globally, ahead of AI development itself. But most of that investment is going into application-layer guardrails built by engineering teams, treating symptoms while the infrastructure underneath stays the same. That’s why 74% are still rolling back agents. Companies can invest heavily in safety and still fail, because the failure modes originate one layer below.

Companies are already looking for alternatives

Enterprises haven’t fully articulated that diagnosis yet, but their behavior suggests they’ve felt it. Eighty-six percent have had active or exploratory conversations with alternative providers in the past 12 months, and only 4% have no plans to evaluate.

The strongest trigger for switching is experience. Ninety-one percent of enterprises that have had to roll back a live agent have evaluated or are actively evaluating a new communications provider. The most sophisticated buyers are the most active shoppers, not because they’re unhappy with a vendor, but because their AI ambitions have outgrown what the current infrastructure was built to handle.

When companies assess alternatives, reliability ranks first with 29% of respondents placing it at the top, ahead of compliance capability, ease of integration, and, notably, pricing. Pricing ranked eighth out of nine factors in the survey.

What this means for the next time you need help

Sixty-two percent of organizations have an AI customer communications agent live, and 88% will have one by the end of 2026.

Getting to production was hard, and most enterprises have made it. But the data is clear: Escaping pilot purgatory wasn’t the hardest part. Many organizations have deployed, they’re scaling, and what they’ve found on the other side is not what the market expected.

For the consumer on the other end of these interactions, the gap is immediate. When an AI agent fails mid-conversation, it often reverts to a human support team, one that was sized for a world where the AI was handling most of the volume. The wait gets longer, the frustration grows, and the brand takes a hit that doesn’t automatically resolve when the system comes back online.

The companies truly pulling ahead in this study aren’t just the fastest to deploy. They’re the ones whose AI stays live long enough to keep improving, backed by communications infrastructure that was actually built for the job.

This story was produced by Sinch and reviewed and distributed by Stacker.

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