Nieman Foundation at Harvard
HOME
          
LATEST STORY
From shrimp Jesus to fake self-portraits, AI-generated images have become the latest form of social media spam
ABOUT                    SUBSCRIBE
Feb. 2, 2022, 11:12 a.m.
Business Models

At the end of 2016, The New York Times had around 1.6 million digital subscriptions. At the time, then-CEO Mark Thompson said that the company’s “ambition” was to hit 10 million digital subs — that it was “possible.” The 10 million figure was soon set as a goal to reach by 2025.

The Times has gotten there early, though, thanks to its acquisition of sports subscription site The Athletic — and The Athletic’s 1.8 million paid subscriptions — earlier this month. (“Separate and apart from The Athletic, the Company believes it would have reached this target well before 2025 on an organic basis,” per the press release.)

The Times also publicly set a new goal of 15 million digital subscribers by the end of 2027 and “plans to increasingly promote a high-value New York Times bundled digital subscription.” (Will the paid bundle include Wordle, which the Times acquired for over $1 million this week? We’ll see, but uh … enjoy playing for free while you can, maybe.)

Additionally, the Times noted that it had “achieved $2 billion in annual revenue for the first time since 2012.” For a reminder of how much has changed, check out this Nieman Lab piece from the time.

Show tags
 
Join the 60,000 who get the freshest future-of-journalism news in our daily email.
From shrimp Jesus to fake self-portraits, AI-generated images have become the latest form of social media spam
Within days of visiting the pages — and without commenting on, liking, or following any of the material — Facebook’s algorithm recommended reams of other AI-generated content.
What journalists and independent creators can learn from each other
“The question is not about the topics but how you approach the topics.”
Deepfake detection improves when using algorithms that are more aware of demographic diversity
“Our research addresses deepfake detection algorithms’ fairness, rather than just attempting to balance the data. It offers a new approach to algorithm design that considers demographic fairness as a core aspect.”