Axios is betting on artificial intelligence to revitalize local journalism, a sector long strained by financial pressures. The media company is integrating AI across its entire workflow, from story ideation to distribution, aiming to deliver high-impact reporting more efficiently and at scale. This strategic use of AI is central to the sustainability and expansion of its Axios Local model.
Allison Murphy, Chief Operating Officer at Axios, explained that AI's capacity for scale and efficiency is a natural fit for their mission to prove a profitable local news model can thrive nationwide. "AI has already become central in how we do the work of Axios Local," Murphy stated in a conversation with OpenAI News. The company has developed a custom GPT, dubbed the "Axiomizer," to assist reporters.
Sharper Reporting, Faster Production
The Axiomizer helps Axios reporters refine their drafts by suggesting sharper headlines and more concise "Why it matters," "What's next," and "Between the lines" sections. This process doesn't replace journalists but enhances their work, making strong reporting clearer and more useful for readers. AI also handles editing and style checks, allowing human editors to concentrate on judgment-intensive tasks rather than basic fixes.
This efficiency gain is crucial for expanding Axios Local's reach. By enabling a single reporter to produce a high-quality local news product in smaller markets, Axios can launch in cities like Boulder and Huntsville, Alabama, which were previously unfeasible. This AI-powered approach allows for more local coverage in more places without compromising quality standards.
Unlocking Public Data and Community Voices
The economic challenges facing local news are significant, largely due to the bespoke nature of community-tailored reporting. AI fundamentally alters this equation by extracting cost efficiencies and maximizing the output of expert staff. It also opens up vast troves of public data, city council minutes, school board recordings, that were previously inaccessible due to time constraints.