Nearly eight years after Epic took on Steam with its own Game Store and launcher, the Fortnite maker is deploying a new tool to give it an edge over Valve: vibe coding.
In announcing the new publicly available "Preview" build of its Epic Games Launcher, Epic writes that "programming is hard. So, instead of doing that, we vibe coded a new Epic Games Launcher that’s way faster and more responsive than the current one, and has a new look." And while that might sound a little tongue-in-cheek, Epic Games Store General Manager and VP Martin Keely told PC Gamer that the team "started from scratch" and used AI as "a tool to write and review new features and new capabilities from the ground up in a different tech stack than what we had previously." The goal, Keely said, was to "look at functionality that existed [in the current Epic Games Launcher] and [say] we want to replicate that, but not the code itself."
Speaking to IGN, Keely clarified that using AI to code the new Launcher "made us more efficient and allowed a small team to rewrite the code base and be able to launch it faster across platforms, support Windows and Mac, and we're going to have a Linux version as well."
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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.
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."
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.
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