Gina Chua, executive director of the Tow-Knight Center (and executive editor of Semafor), with a sweepy post on the journalism industry's contorted view of AI's potential. She makes a lot of great points but my takeaway was this:

If the starting point of the debate is what-can-humans-do-better-than-machines, rather than what-does-the-public-we-serve-need, then we’ll end up prioritizing ensuring we have jobs, rather than ensuring we serve communities well.

That’s also an overstatement, of course; and I’ve written many times about the multiple roles I believe humans need to play in the coming information landscape.

But the fundamental issue is a dogmatic belief that machines can’t surpass us, in the face of a body of evidence that they already do in a range of processes.

AI can be both a threat to the news business, or it can be an incredible tool for streamlining some of the processes that go into everyday reporting. Here's Nieman Lab on the New York Times using AI to scrape the so-called "manosphere" for shifting reactions to the Jeffrey Epstein files:

Built in-house and known internally as the “Manosphere Report,” the tool uses large language models (LLMs) to transcribe and summarize new episodes of dozens of podcasts.

“The Manosphere Report gave us a really fast and clear signal that this was not going over well with that segment of the President’s base,” said Seward. “There was a direct link between seeing that and then diving in to actually cover it.”

Read it all: How The New York Times uses a custom AI tool to track the “manosphere”