Meta Says AI Coding Is Accelerating a New Wave of Consumer Apps

After years of failed incubator experiments, the company believes faster development and its recommendation systems can improve the odds of finding another breakout.

By The Company WireJuly 30, 20262 min read

Meta — Meta Says AI Coding Is Accelerating a New Wave of Consumer Apps. Mark Zuckerberg, chief executive officer of Meta Platforms Inc., during the Meta Connect event in Menlo Park, California, US, on Wednesday, Sept. 25, 2024. Meta Platforms Inc. debuted its first pair of augmented reality glasses, devices that show a combined view of the digital and physical worlds, a key step in Chief Executive Officer Mark Zuckerberg’s goal of […].

Meta is using large language models to shorten the time required to build and test consumer applications. CEO Mark Zuckerberg told investors that the company has additional products in development after releasing standalone tools for Marketplace sellers, Facebook Groups, games, photos and experimental AI experiences.

The strategy revives a problem Meta has struggled with for more than a decade. Earlier incubators produced apps including Slingshot, Rooms, Paper, Tuned and BARS, but none developed into a lasting independent platform. Most were closed after failing to attract enough users.

AI changes the cost of experimentation. Smaller teams can generate code, test interfaces and ship updates more quickly, allowing Meta to evaluate more ideas before making a large commitment. The company also has recommendation systems and existing social graphs that can place a new product in front of hundreds of millions of people.

Distribution does not guarantee retention. A fast-built app still needs a distinct reason to exist, and Meta's history shows that users may not adopt a separate product simply because it is connected to Facebook or Instagram. Threads, now reporting 500 million monthly users, is the strongest recent example of the company turning its scale into a credible new network.

The internal development process may change Meta's workforce as well. Teams that can launch with fewer engineers may produce more experiments while concentrating decision-making among product leaders and model operators. Meta should track whether faster coding improves quality or simply moves the bottleneck into review, privacy testing and long-term maintenance.

Faster internal development could let Meta test products for smaller communities that would not justify a large traditional team. That is useful if experiments have clear ownership, privacy review and a plan for user data when they close. The company's history of shutting incubator apps makes those safeguards important. AI-generated code can also create security and accessibility defects at scale if review does not keep pace. Meta should report whether the new process reduces time to a durable product, not only time to an initial launch. Shipping more is not the same as learning more.

The approach could produce useful products, but it could also create more short-lived software and fragmented privacy settings. Meta should be clear when an app is experimental and provide predictable shutdown and data-export policies. AI makes launching easier. It does not remove the company's responsibility to support products after users invest time and information in them.