Meta says it will launch more apps because artificial intelligence makes development faster. CEO Mark Zuckerberg told investors on Wednesday’s earnings call that the company has several new products in the works, following recent releases including a tool for Marketplace sellers, an application for Facebook Groups, a vibe-coded gaming title, a new photos app from Instagram, and an experiment with AI bedtime stories.
Meta has spent years trying and failing to produce standalone social apps to complement its core platforms. The company now says large language models make it possible to ship software faster, allowing it to test new ideas at a quicker pace.
“I’m…excited about how AI is helping our teams speed up product development,” Zuckerberg told investors. “Earlier this year, we shipped Instagram Instants. We also just launched Forum, a standalone Groups app, and Seller, a standalone Marketplace app. I expect it to become a lot easier to ship new apps. So we are planning to build out more ideas and use our recommendation systems to scale them,” he said.
“AI is improving our core business; it’s making our apps more relevant and delivering better results for businesses. We’re starting to deliver more novel products, and we’ll have a lot more there soon as well,” Zuckerberg said.
History of failed experiments
Meta has been down this road before. In its earlier days, Meta (then known as Facebook) ran an internal incubator called Creative Labs, which aimed to test new social concepts.
That effort produced a handful of launches: the photo-sharing app Slingshot, an anonymous chat app Rooms, a Flipboard competitor called Paper, the Moments photo-sharing app, and a collaborative video app known as Riff. Those experiments came to an end in 2015, and the apps were eventually all shuttered, as the company struggled to find an audience for its efforts.
In the early 2020s, Meta tried again, this time with an internal R&D group, NPE Team, which tested apps that included the chat app Bump, social music app Aux, task app Move, dating app Spark, calling app CatchUp, zine maker E.gg, events app Venue, creator Q&A app Hotline, Cameo competitor Super, couples app Tuned, music app BARS, and others.
Again, none became a breakout success, and the apps were shut down.
Threads as a proof of concept
Now Meta can point to at least one example of how AI is helping new apps scale. It has finally delivered a modest hit with Threads, which now has 500 million monthly active users. Zuckerberg likes to say Threads will one day become the company’s next billion-user app.
With Threads, Meta learned to heavily lean on its existing user base to help initially seed the app with people, then continued to heavily promote it across its existing platforms, including Facebook and Instagram. But LLMs are another key factor in Threads’ growth, as the company said it sees “significant gains” from its AI-powered content recommendations.
“We are finding that LLMs are increasingly capable of delivering ranking and recommendations gains,” Meta’s CFO Susan Li told investors on the call. “First, they make our existing systems smarter by understanding what the content is actually about and generating better training data. Second, LLM-powered agents are also helping with engineering development by evaluating content quality, detecting trends, and testing ranking changes.”
Li added that earlier this year, Meta reached a milestone: every Reel and Feed post on Instagram is now automatically processed through an LLM and analyzed for topic and tone, which helps improve recommendations.
The company is also developing LLM-native recommendation systems, which could help it to better scale new apps as they arrive.
Investors didn’t follow up with company executives to ask more questions about the new apps Meta has in the works, as they were more concerned with AI spending and Meta’s growing enterprise ambitions. However, Zuckerberg suggested that people won’t have long to wait to see what’s next, saying the “new consumer products” were “releasing soon.”
What it means
For people building things, the shift is simple: development time shrinks because AI handles more of the heavy lifting in engineering and data analysis. Teams can move from concept to launch faster, but the strategy remains the same. Success still depends on getting users to download the app and keeping them there through good recommendations.
