It also carries obvious risk: He’s trusting that machines will be able to determine what is true, what is newsworthy, and what won’t get him sued. One of his agents runs an analysis on the legal risk a story poses and assigns it a score; he doesn’t publish anything deemed too dangerous.
He isn’t the only person running this type of exercise. Dakota Carrasco, a BlackRock portfolio analyst, runs an “agentic newsroom” called The Dissent in his spare time. Like RuntimeWire, The Dissent is a one-man, many-bot operation with a shoestring budget. Its primary San Francisco-focused site costs under $1,000 a month to run, Carrasco says. Unlike Merket, who bylines every RuntimeWire story, Carrasco doesn’t publish the writing under his name, instead staying behind the scenes while his “newsroom” runs.
Since launching in March, he has created a number of personalities for his synthetic journalists. City Hall beat reporter Bex Connolly, for example, is “skeptical without being snide,” while “sports degenerate” Sal Moreno delivers Giants news with “no bro-science, no Rogan-style credulity, no right-coded grift.” His operation is focused on aggregation, though it’s not as dialed in to honoring citation norms—the bot reporters tend to mention where they sourced their information but without hyperlinks. (“I’m trying to work on that,” Carrasco promises.)
Northwestern professor Nicholas Diakopoulos, who runs the university’s Computational Journalism Lab, sees this as an “experimental phase” for media startups fueled by generative AI tools. “It’s not yet clear to me that there’s much audience for these AI-agent-written news sites,” he says. He’s also skeptical that mainstream journalists, who like to maintain control over the wording and framing of their stories to ensure integrity, legality, and accuracy, would hand the reins over to AI agents so freely.
What Diakopoulous has observed already is that when AI chatbots go looking for sources, they frequently pull up AI-generated articles. In a forthcoming paper, Diakopoulous and a colleague found that AI tools like ChatGPT and Claude surfaced AI-written sources 16 percent of the time when they tested it across four different topics. AI’s willingness to pull synthetic writing may help AI newsrooms find readers, he suspects: “That could be one way in which some of this material finds a human audience.”
Pete Pachal, the founder of a newsletter and podcast about generative AI and the media, has doubts that an AI newsroom could yield certain types of reporting that relies on old-fashioned sourcing. “I just don’t see that happening,” he says. “Cultivating the trust of a source, I do think that’s going to be human-only.” But for certain types of journalism, particularly sourcing scoops from large datasets or even blogging about a live event like an Apple product launch, he sees these projects as a “natural evolution” in how these tools are used. “Honestly, it feels a bit inevitable,” he says.
Is it actually journalism, though? “I am trying to follow journalistic ethics and standards,” Merket says. He says he contacts companies and individuals referenced in the stories for comment prior to publication, links out to sources when he aggregates news, and issues corrections if he gets the facts wrong. (So far, there have been three.) Sometimes he speaks like a reporter, too: “This weekend, I got two scoops up I was really excited about.” Other times, though, he’s more clearly in Silicon Valley mode. He told me a story about how his AI agents had found a few actual scoops about startups by trawling company websites and that he’d retracted the stories after the companies named asked him to do so—not because they were inaccurate, but as a favor. “Founder to founder, it’s like, I get it,” Merket says.