Reuters treats AI as a research assistant that must show its work. The BBC requires a human sign-off before any AI-assisted story runs. The Guardian will only let staff use it once a senior editor has approved the specific task. Three of journalism’s most prominent outlets, three distinct answers to the same question — and together they show there is no single blueprint for the AI-powered newsroom.

A new comparative study published by Nieman Lab examined how Reuters, the BBC and The Guardian have approached AI deployment, finding that each outlet has built its own framework around editorial priorities, governance and audience trust rather than embracing a uniform set of tools. The research analyzed public AI policies, editorial guidelines, trial reports and industry presentations released through 2025.

The study suggests AI adoption in journalism is increasingly less about whether to use the technology and more about how news organizations establish controls over its use.

Reuters has positioned AI as a reporting assistant that speeds journalism without replacing editorial judgment. Its in-house tools include Lynx Insight, which analyzes financial data in real time, and Fact Genie, which helps reporters process corporate press releases. Journalists must independently verify AI-generated claims before publication under its standards. Market reporting also provides unusually fast error-correction signals, since traders spot bad information quickly — a less forgiving model than political reporting or investigations, where a factual error can sit unnoticed far longer.

Reuters Editor-in-Chief Alessandra Galloni has described AI as a “force multiplier” that helps journalists analyze large document collections and identify patterns more efficiently, with governance checkpoints built in at each stage to preserve editorial accountability.

The BBC has taken one of the industry’s most cautious approaches, prioritizing transparency over rapid deployment. It permits AI for specific tasks such as At a Glance, which summarizes long articles, and BBC Style Assist, which reformats local stories in house style — a task the broadcaster says can otherwise take about 30 minutes. No AI-assisted material runs without an editor’s sign-off first.

That caution is evidence-led: in October 2025, the BBC and the European Broadcasting Union published research showing AI assistants misrepresented news content 45% of the time through faulty sourcing, fabricated details and outdated information. The broadcaster later introduced AI disclosure labels at the top of stories.

The Guardian revised its guidance in March 2026 to permit limited use of generative AI for tasks such as generating image alt text, analyzing parliamentary documents and transcribing audio, provided the tools meet editorial standards and a senior editor approves where required.

The policy reflects the outlet’s broader philosophy that AI should support, not replace, journalists. “Our generative AI framework is designed to support our journalists’ expertise, never replace it,” the outlet said in a statement. “Our journalists are always accountable for the journalism they create, and use of these tools requires absolute rigor and responsibility.”

The Guardian’s reader-funded trust ownership model has also shaped its strategy, allowing the publisher to prioritize editorial independence over commercial pressure even as the newsroom gradually expands its use of the technology.

The findings illustrate an industry shift away from viewing generative AI primarily as a content-generation tool. Leading publishers are increasingly deploying it behind the scenes — for research, document processing, transcription, translation and newsroom efficiency — while keeping journalists responsible for editorial decisions.

The divergent strategies also reflect broader debates across the news industry over governance, transparency and the economics of AI. As publishers negotiate licensing agreements with technology companies while pursuing litigation over unauthorized use of copyrighted reporting, many are simultaneously developing internal policies governing how AI can be used inside their own newsrooms.

Nieman Lab’s analysis concludes that newsroom AI adoption is evolving into an organizational question rather than simply a technological one. Success, the report argues, depends not only on selecting AI tools but on establishing editorial policies, accountability mechanisms and workflows that preserve journalistic standards as AI becomes part of everyday reporting.

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

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

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

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

Transparency has a price tag

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

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

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

Three forces are pushing the AI byline out.

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

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

What the byline really is

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

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

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

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

Human accountability is the scarce signal

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

A version of this column appears in Fast Company.

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Reuters Editor-in-Chief Alessandra Galloni says the news agency is already using AI to draft headlines and opening paragraphs, translate copy and distill information from press releases — offering one of the clearest examples yet of how generative AI is reshaping newsroom workflows.

Speaking after delivering the Andrew Olle Media Lecture at Australia’s ABC on July 23, Galloni told the Australian media industry news website Mumbrella that Reuters has incorporated AI into routine production tasks while keeping reporting and editorial judgement in human hands. She argued that offloading grunt work to AI doesn’t strip young journalists of a way in. It just changes what the entry point looks like.

Three decades ago, Galloni said, junior reporters spent much of their time on administrative tasks that AI can now automate. Today’s newcomers, she argued, are instead building skills in data journalism, visual verification, camera work and drone operation.

“There are many things that you can do that didn’t exist 30 years ago, like going into the visual verification team or the fact-checking team of a news organization,” she said.

Her broader point was that the core of the profession remains unchanged. Rather than spending their first years on repetitive production work, Galloni said young reporters should focus on finding stories, interviewing people and developing reporting instincts.

“The best thing that young journalists can train on is going out and finding news and talking to people,” she said. In her view, AI is simply another tool modern reporters need to learn, alongside a growing set of digital reporting skills.

For newsrooms watching their entry-level pipeline shrink alongside AI adoption, Galloni’s comments are a rare instance of a wire service leader naming the tradeoff directly rather than dodging it. Reuters isn’t hiding that AI is contributing to the drafting and translation work once assigned to junior staff. The bet is that those time savings are redirected into reporting, verification and technical skills AI can’t replicate.

Whether that bet pays off depends less on the technology than on newsroom investment. Automating routine work only builds better journalists if editors reinvest that freed time into mentorship and field reporting, rather than treating it as a headcount reduction.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Layoffs in the U.S. and U.K. journalism industry topped 2,300 newsroom jobs in the first half of 2026, Press Gazette reported July 14, a trend that, if it continues, represents an almost 34% increase over all of 2025’s journalism job losses. The majority of jobs lost are among public broadcasters and wire services alongside newspapers, magazines and digital publishers.

The Press Gazette’s running tally, tracked at least 3,434 jobs lost in 2025 and at least 3,875 in 2024.

Publishers increasingly describe the changes as newsroom reorganizations rather than cost-cutting measures, the underlying pressures remain visible. As audiences migrate to video, social platforms and AI-powered search and chat interfaces, news organizations are redirecting resources toward digital products, audience growth and new distribution strategies. 

The BBC has said it plans to eliminate up to 2,000 positions, roughly one in 10 employees, in what would be the broadcaster’s largest workforce reduction in 15 years. BBC News is expected to bear much of the impact as the organization seeks hundreds of millions of pounds in savings. 

In the U.S., NPR is cutting up to 30 newsroom positions while offering buyouts to hundreds of employees as it meets shifting audience habits and the loss of federal subsidies. The Associated Press is also reducing its editorial workforce through a combination of buyouts and layoffs as it shifts investment toward visual journalism and digital products. 

Other publishers have announced smaller but persistent reductions. CBS News cut about 66 employees and has shut down its century-old radio division. The Washington Post is shrinking parts of its newsroom and business operations, while Vox Media, Condé Nast, Future, Politico, Bustle Digital Group, The Standard, and Nexstar Media Group have also reduced their editorial staff this year. 

At the same time, many are expanding their use of AI to automate production workflows, personalize recommendations and support reporting, raising questions about the fate of the traditional newsroom structure as emerging technologies reshape the industry.   

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

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

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

Getting into the citation pool early

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

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

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

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

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

The unit of competition has changed

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

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

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

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

Reporting is still the moat

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

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

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

A version of this column appears in Fast Company.

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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.”

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I’ve been working with Claude Cowork extensively over the past month and a half. And not coincidentally, I’ve found myself accomplishing more during this period than at almost any other time in my career. The shift toward agentic work represents a transformation so fundamental that its impact is difficult to grasp until you actually experience it.

Just one example: As someone running a business that sells AI training courses online, email marketing is an important component of reaching potential customers. But the work itself is tedious: segmenting my email list, creating templates, writing largely similar drafts, and scheduling them in my email provider—a piece of software I look forward to using about as much as a visit to the dentist.

Now I hardly ever touch that software; Claude Cowork does it for me. When you have access to agents, you can loop them in on any computer task with three beautiful words: “You do it.” AI doesn’t just draft emails for me—it puts them in the campaign builder, targets the right audience, gets all the settings right, and then taps me on the shoulder (via a notification) so I can approve the work before it schedules everything to go out. Once you start working with agents, you quickly start crossing things off your to-do list faster than ever before.

Becoming the CEO of your job

This represents more than accelerated productivity. It’s a fundamentally different way of working. Instead of personally grinding through individual tasks, the focus shifts to defining desired outcomes, delegating execution to digital workers, and evaluating their output. Instead of simply doing your job, you become the CEO of it, delegating many tasks to agents.

So what happens to a newsroom when everyone starts working agentically? Over the past 30 years, reporters and editors have needed to become skilled at many different systems: project-management software for tracking stories, content management systems for publishing them, SEO plug-ins, social media management platforms—the list goes on. Agents open the possibility that journalists could instruct them to manage all of this infrastructure while they go and do the important, human-centered work of reporting and editing.

But the complications emerge when this same agent model gets applied to journalism’s core function: writing itself. This came to a head recently with the uproar over what The Plain Dealer, Cleveland’s primary newspaper, is doing: leveraging an AI writing agent so reporters can simply feed notes and context to create stories. To be clear, all the stories are then edited, and the reporter has final say over the copy. But applying agents this way brings up hard questions about jobs, skill-building, and career paths.

Yet beyond this specific scenario lies a broader reality: agents will almost certainly assume much of the repetitive work surrounding content creation and distribution. Whether it’s social media management, SEO (and GEO), or getting all the little drop-down menus, boxes, and tag fields in your CMS just right—those are all jobs for agents. More importantly, roles that are centered around optimizing those tasks will gradually go away.

Consider what happens: when search and social platforms drive audience discovery, newsrooms organize work around those algorithmic preferences. Many roles emerged that were simply writing to a trend, publishing undifferentiated “quick hits” around trending topics to maximize clicks. Those jobs were effectively hyper-optimizing production of formulaic stories, writing for algorithms and chasing virality through pattern recognition. An AI system can accomplish this faster, at higher volume, and more efficiently than any human.

Here’s the paradox: this development might actually prove beneficial for journalism—something I predicted in a column I wrote almost exactly a year ago. Agents are a crucible for knowledge work, burning away anything and everything that can be automated, leaving only the parts of the job that can’t be easily repeated—the work that requires either creating new information or judgment, context, and taste.

The agentic newsroom

If you were designing a newsroom optimized for AI from the start, using this principle, the bulk of positions would focus exclusively on the distinctly human elements: cultivating trust with sources through direct access and personal relationships, conducting original reporting and uncovering information exclusive to your brand, determining which stories resonate most with audiences and which narrative angles matter most, and applying the craft of storytelling across all of it.

While that sounds appealing in certain respects, the economic reality is harder: with agents executing most of the work, fewer jobs will likely exist. In almost all cases, organizations will be smaller, with different career paths, even if the work is richer.

A current limitation is the scope of what agents can access. Tools like Claude Cowork and Claude Code become truly powerful only when they can move beyond drafting and into systems (email, CMS, analytics, internal documents). That is where most organizations get uneasy. Granting an agent permission to act inside those environments raises questions about security and accountability. Most teams are still feeling their way through this, limiting agents to narrow tasks or read-only access. But that tension is temporary. As guardrails improve and familiarity grows, those permissions will expand, and with them, the scope of what agents can do.

The fundamental premise remains unchanged: journalism’s purpose is not threatened. Instead, its true essence becomes visible when machines handle the repetitive parts. An AI-first newsroom doesn’t mean a less human one. In fact, it means the opposite. When the repeatable work is handled by machines, what remains is the work that defines the craft: earning trust, finding new information, and making sense of it for an audience. The uncomfortable part is that there may be fewer people doing that work. The hopeful part is that the work itself becomes more meaningful.

A version of this column appeared in Fast Company.

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

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

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

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

What Mythos Preview is — and who can use it

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

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

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

Why regulators are moving fast

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

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

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

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