What's the most important question right now?

How is my newsroom going to weather the erosion of news aggregation?

Editorial roles centered on aggregation — curating and synthesizing existing reporting — are among the jobs most at risk of displacement by generative AI tools and by startups operating outside traditional news organizations.

Over the past decade, an era often defined by social media traffic and SEO gamesmanship, newsrooms invested heavily in editorial teams that aggregated rather than producing original journalism. News sites were flooded with stories recycling other outlets’ reporting for their own audiences, or with articles made entirely of reworked press releases and curated social posts. The business models of digital journalism incentivized this kind of aggregation. Even now, many editorial staffers spend an entire day producing aggregations without ever speaking to a source.

Generative AI tools cannot displace shoeleather reporting. Whether they should be or not, these tools are increasingly being used to automate significant parts of the aggregation workflow.

I’ve been reporting on networks of hundreds (and even tens of thousands) of AI-generated local newsletters spreading across the U.S. At Good Daily and Patch, daily roundups that were previously produced by dozens of writers and editors are now fully automated. Startups like NoahWire and Open Mind are experimenting with automating parts of the discovery, drafting, and even fact-checking for aggregated articles. In all the chatter about newsroom displacement, I think aggregation still feels sorely underestimated as a space on the cusp of a major reinvention.

What will we be shaking our heads about a year from now?

We’ve devoted so much of our strategic energy to how AI tools might deliver minimal gains in newsroom productivity — without properly sandwalling our editorial operations against the rise of generative search.

In my view, the so-called “traffic apocalypse” hasn’t been borne out in the numbers… yet. That said, I welcome the urgency and attention recent coverage has brought to how generative AI tools are changing our audience landscape. There is good reason to be sounding the alarm.

Until recently, though, most newsroom leaders I’ve spoken to have been more concerned with whether or not a ChatGPT Enterprise account would shave hours off an investigation, than about whether generative search products will kill the subscription conversions that ultimately fund that reporting once it’s published.

What future are you looking forward to?

When machine learning techniques long pioneered by data journalists are accessible to the average reporter.

Over the past couple years, I’ve profiled a handful of Pulitzer Prize winners and finalists that disclosed using AI in their work. These adoption use cases run the gamut, including creating complex data visualizations and training custom object detection models. One trend that stood out: some awardees were using off-the-shelf tools, or using conversational AI models that required no knowledge of Python or R.

The barrier to entry for using machine learning is lowering by the day, and I can’t wait to see more journalists without engineering backgrounds get their hands on these powerful reporting tools.