How Axios Local is leveraging AI to expand into smaller cities
Editor Holly Moore explains her blueprint for leaner local newsrooms
When Axios launched its local division, the premise was deceptively simple: take a national media brand built on email distribution and “smart brevity,” and apply that same model to city-level journalism. Instead of chasing scale through pageviews, Axios Local would hire well-sourced reporters, deliver high-signal updates directly to inboxes, and monetize through a mix of local and national advertising. The approach proved that a lean, newsletter-first product could build meaningful audience relationships in major metro areas—without relying on the brittle economics of traditional local news.
But the model also came with a constraint. It worked best in larger cities where advertising demand could support multi-reporter newsrooms. Now, Axios is attempting something more ambitious: pushing into smaller cities, suburbs, and even news deserts—markets that historically haven’t supported sustainable journalism. As Axios Local executive editor Holly Moore explains, that expansion hinges on a fundamental shift in how local newsrooms operate, with AI playing a central role in reducing costs, increasing efficiency, and enabling a single reporter to cover what once required a full team.
In a recent interview, Moore explained how Axios is using AI to shrink newsroom staffing requirements in smaller markets, why the company is experimenting with regional coverage models that stretch a single reporter across multiple geographies, and how its long-term ambition could see it expand into hundreds of cities.
Let’s jump into it…
From Charlotte Agenda to a National Playbook
Axios Local’s origins trace back to its acquisition of the Charlotte Agenda, a digital-native local publication that had already cracked a key problem in media: how to build strong brand affinity within a specific geography. Charlotte Agenda succeeded by targeting a clearly defined audience—young professionals—and delivering a mix of original reporting, curated information, and lifestyle content tailored to daily life in the city.
Axios recognized that this model could be replicated. “If Charlotte can have this much success, then we can replicate it in markets across the country,” Moore said.
The company layered its own product philosophy on top of that foundation. “Smart brevity”—Axios’s signature style—became the organizing principle for local newsletters, emphasizing concise writing, scannable formats, and a mix of original reporting and curated updates. Crucially, distribution was anchored in email, giving Axios direct access to readers without relying on social platforms or search traffic.
This inbox-first approach also aligned neatly with advertising. Because Axios collects first-party data on both geography and subject-matter interests, it can offer highly targeted sponsorships—allowing advertisers to reach, for example, tech professionals in Denver or business leaders in Chicago.
The Original Model: High-Touch, Reporter-Driven Newsrooms
In its first phase, Axios Local followed a relatively high-cost editorial model. Early markets like Denver and Chicago launched with three to four reporters, each bringing deep sourcing and subject-matter expertise.
The editorial mix was intentionally balanced. Each newsletter included a “news roundup” that curated the most important local stories alongside a more substantive piece of original reporting. Data-driven visuals and charts added another layer of value, helping readers quickly digest complex information.
Workflows varied by market, but most teams divided responsibilities to preserve reporting capacity. One reporter might take the lead on assembling the daily newsletter—curating links, writing headlines, and structuring the edition—while others focused on original reporting. Editors, often overseeing multiple cities within a region, handled copy editing and quality control.
The result was a product designed to feel both authoritative and personal. Reporters were not just bylines—they were embedded in their communities. “They live there. They see these people in the grocery stores,” Moore said. “They respond to their emails.”
That connection extended into the product itself. Each newsletter included small personal touches—brief intros, anecdotes, or weekend plans—that helped humanize the reporter and deepen audience trust. Over time, this relationship became a defining feature of the brand, differentiating Axios from more transactional news products.
The Scaling Problem—and the Shift to Smaller Markets
Despite its success, the original model had a clear limitation: it was expensive. Headcount is the largest cost in journalism, and sustaining multi-reporter teams requires robust local advertising markets—something many smaller cities lack.
This raised a fundamental question: could Axios Local expand beyond major metros into smaller or underserved areas?
The company began testing this with markets like Huntsville, Alabama, launching with a single reporter instead of a full team. The early results were encouraging. Audience response was strong, and the need for local news was evident. “They’re so happy we’re there,” Moore said. “They definitely feel like they’re being served.”
But the experiment also exposed the strain on individual reporters. Covering an entire city alone—while also producing a daily newsletter—requires significant bandwidth. To make the model sustainable, Axios needed a way to reduce the workload without sacrificing quality.
AI as an Operational Layer, Not a Replacement
That’s where AI enters the equation—not as a replacement for reporters, but as an operational layer designed to handle repetitive and time-consuming tasks.
Axios has focused its AI efforts on areas where automation can deliver immediate efficiency gains. One example is news curation. The company has built tools that surface relevant local stories—essentially giving reporters a head start on assembling their daily news roundups. “We’re not pulling directly from the tool into the newsletter,” Moore explains, “but it does give a head start.”
Another major focus is production. An internal tool dubbed the “Axiomizer” acts as an AI-powered copy editor, applying Axios’s style rules and grammar standards before a piece reaches a human editor. This reduces the burden on editors who oversee multiple markets.
AI is also being used to streamline data-driven content. Axios’s earlier “Hub” team created templated stories based on national datasets, which reporters would localize for their markets. Now, the company is experimenting with automating that localization process—allowing reporters to review and refine output rather than build it from scratch.
Perhaps the most promising applications lie in information gathering. Reporters are using AI to transcribe public meetings, summarize lengthy documents, and identify key moments in video recordings. The company is also exploring tools that can assist with FOIA requests, from drafting inquiries to analyzing responses.
Across all these use cases, one principle remains central: “human in the loop.” Every piece of content is reviewed by a reporter before publication. “Don’t let anything get out there that hasn’t gone through a person,” Moore said.