You Built an App With AI. Now What?

New app launches roughly doubled on the App Store in the first half of 2026, with around 560,000 new apps shipped, according to Sensor Tower data reported by 9to5Mac. Downloads over the same period grew about 2 percent. Read those two numbers together and the picture is obvious: a lot more software is going out the door, and almost none of the extra supply is finding extra users. Vibe coding and AI app builders get a lot of credit for the launch surge. They get almost no attention for what happens next.

Build-time is the easy part now

This is worth saying plainly because every AI app builder, including this one, markets the build step. One sentence in, a working app out, in minutes instead of months. That part is genuinely true now, and it's genuinely new. But an app isn't a single event. It's a thing that needs fixing when something breaks, updating when the underlying platforms change, and extending when real users ask for something the first version didn't do. None of that goes away just because the first version got built fast.

What the data on AI-generated code actually shows

This part deserves real caution, because a lot of the numbers circulating about AI code quality come from blog posts and forum threads summarizing other work, not from the original studies themselves. With that caveat up front: one analysis of AI-introduced code issues found that roughly 22.7 percent of them survive into the latest version of the codebase rather than getting caught and fixed, and by early 2026 more than 100,000 unresolved AI-introduced issues were still sitting in production codebases, some for over nine months. Separately, a study covering 153 million lines of code reported that the share of AI-assisted code rewritten or deleted within two weeks of being written rose from 33 percent to 40 percent, though this figure comes from a secondary summary and I couldn't verify it against the original dataset. Treat both numbers as directionally useful, not as settled fact. What they point toward, consistently, is that code which looked finished at build-time keeps needing real, ongoing attention afterward, and that attention is where a lot of the actual cost of an AI-built app lives.

What this actually means if you just built something

• The tool you used to build the app matters more after launch than before it. A builder you can go back to for a real fix, not just a one-time generation, is doing the harder half of the job.
• "It looked right in the preview" and "it works on a real device against a real server" are two different claims. The second one is the one that actually matters once real users show up, and it's worth checking directly rather than assuming.
• Budget real time for the weeks right after launch, not just the weeks before it. Crash reports, platform API changes, and edge cases real users find are normal, not a sign something went wrong with how the app was built.
• Ask, before you commit to a tool, what happens when you need to change something six weeks after shipping. If the honest answer is "regenerate the whole app and hope," that's worth knowing before you're six weeks in, not after.

Why we publish failures, not just successes

This is the specific thing we do differently, and it's a direct response to the gap above: every test result on Burgndy.ai's build pipeline is public, including the ones that failed, not a curated highlight reel. An AI app builder that only shows you its best outputs can't tell you anything about what happens when something doesn't go cleanly, and post-launch is exactly where that honesty matters most. Our first validated batch hit 8 out of 8 successful builds, verified against live servers rather than self-reported, and we keep publishing that way as the product keeps running, not just on launch day.

None of this is an argument against building fast. It's an argument for picking a build method you can actually live with once the easy part is over, because for every app that ships, the easy part ends the same week it starts.