In the AI age, publishers who understand what readers want will do best, a new report argues

7 hours ago 3

AI inverts the traditional journalism hierarchy: Instead of reporters gathering information, editors deciding how to present it, and newsrooms releasing the same final story to everyone, AI agents deliver what readers ask for, refining or expanding on information as requested.

It’s “an interactive process in a way the current news system isn’t,” write the authors of “The Three-Layer Cake: Public-Interest Information in an AI-Intermediated Age,” released Wednesday by the Tow-Knight Center for Journalism Futures at the Craig Newmark Graduate School of Journalism at CUNY. (The cake’s three layers are “the products that identify what people want to know, the systems that synthesize answers, and the organizations that produce the underlying reporting.”)

Some interesting ideas from the report by Gina Chua and Adiel Kaplan:

This new age may be better for some readers. The authors write:

There are people the old system never reached, or never reached well. A parent who hears of school budget cuts may want to know if their child’s elementary school art class will survive. No newsroom would assign that story. It matters to a handful of families whose children attend a single school, and the exact answer is uncertain. A well-designed AI system could pull together what is known from reporting and context and offer a nuanced response — fulfilling an information need that previously did not have enough economic power to be met directly.

Similarly, minority communities whose concerns didn’t rise to the level of interest of a city newspaper might have rarely seen their perspectives reflected in coverage in the past. Coverage in the mainstream media of the AIDS epidemic in the early 1980s was often focused on the risk to the heterosexual population rather than on the gay community it ravaged. An AI system that can access information from and about underserved communities could rectify that.

Publishers opting out could make the information environment worse. If news organizations seek to prevent AI from using their content, they may contribute to a poorer “information commons,” the authors argue. It “works as a strategy for individual organizations, but cedes the broader mission of providing information to the vast majority of the public to others who may not have the public’s interests at heart.”

Scoops and exclusives are a “luxury” — and they’re losing value fast.

Even a pivot to investigative reporting, as important a public good as that is, is not necessarily a solution. Much of investigative journalism is summarizable once it is published, even if the original story breaks important news and is a delight to read. As Francesco Marconi noted, a multi-month investigation could cost a newsroom $200,000 in resources, then drop in economic value to close to zero within minutes of publication. Both investigations and scoops can have huge social value and help build a brand, but economically, they provide little lasting value to their newsrooms — and even less so in an AI-intermediated landscape.

“Commoditized news” isn’t saving anyone. Publishers have to make “hard choices” about what they’ll cover, the authors write:

In an AI-intermediated world, commoditized news carries little value, especially if it’s already available in some other form. That’s not to say that day-to-day coverage of municipal government, a core task of many local newsrooms, should go away; only that its coverage needs to be differentiated from what could be found elsewhere or summarized from a meeting transcript, for it to hold value. Differentiation might involve adding context to the coverage through original reporting, or asking questions that wouldn’t otherwise be asked at a press conference.

The deeper and more specialized a newsroom’s area of coverage and expertise is, the less likely it is to be duplicated by other organizations — and that provides both pricing power and closer links to the communities that value such information.

The report is here. Read excerpts from a talk that coauthor Chua gave to Nieman Fellows here.

Read Entire Article