Google has already added a few AI features to the Play Store, but a lot more are coming. The previously released Ask Play feature is expanding to offer more AI-powered discovery options in Google's app store, and Google is also using AI to build a recommendation engine called Game Finder. For people who have embraced AI to build their own apps, the Play Store will soon offer a way to privately host and share those creations.

Google rolled out Ask Play in 2025 as a small chatbot interface on Play Store listings. It lets you ask questions about an app or game, either by typing it in manually or choosing from a list of suggested queries. Starting today, Ask Play is expanding into a more general way to search for apps and games. You ask the robot questions, and it generates recommendations that can include detailed comparison charts. Gemini will roll out a similar capability soon.

Google's other AI discovery announcement focuses on games. The new Game Finder feature leans on all the usage data Google's app platform has hoovered up over the years, featuring insights into the way 1.2 billion people play games on Android. It builds a profile called Gamer DNA and matches you with players who have similar DNA.

Read full article

Comments

Anyone who has even tangentially associated with computer programming knows that modern AI coding assistants and agents can be incredibly efficient at generating huge amounts of functional code. But coders making use of those tools also know better than to trust the accuracy of that code, meaning substantial effort needs to be spent reviewing any AI-generated output.

A recent study of actual coding practices across hundreds of firms finds that human code review forms a significant "bottleneck" for the overall efficiency of AI coding tools, resulting in "little evidence that firms increase software output or reduce employment" by using them. Any efficiency increased during the actual coding phase, the study authors find, is "absorbed by downstream constraints in the production process"; as "the code review process significantly increases in length, pull requests are more likely to require revisions, and reviewers leave more comments."

Cut once, measure twice

To come to these conclusions, Harvard University researchers Fiona Chen and James Stratton made use of aggregated analytics data from Jellyfish, which measures the granular output of engineering teams. That data encompasses 300 million individual "work events" (e.g., commits and pull requests) and issue management software data across more than 700,000 employees at over 700 relevant software development firms from 2021 through March of 2026.

Read full article

Comments