The Build-Abandon Loop: Why Vibe Coding Users Start Projects and Never Come Back
You’ve seen the usage curve on vibe coding platforms. New project created. Daily sessions for a week. Then silence. Then a new project. Daily sessions. Silence. Repeat.
The user never left. They’re technically still active, still paying, still opening the app. But they’ve shipped nothing. They have a graveyard of half-built apps and zero deployments. And your retention dashboard has absolutely no idea.
This is the build-abandon loop. It’s the most common behavioral pattern on vibe coding platforms, and it’s masquerading as engagement.
^ every vibe coding platform’s DAU chart, hiding what’s actually going on underneath
What the loop looks like in practice
A user opens your platform with an idea. A side project, a weekend app, something they’ve been meaning to build. The first session is electric. The AI scaffolds the whole thing: landing page appears, buttons work, the database is wired up. They send a Loom to their co-founder. Real momentum.
Session two, they add features. Still fast. The AI handles it.
Session three, things slow down. A weird auth bug. An edge case the AI cant quite explain its way through. Session is 40 minutes but nothing ships. They close the tab a little frustrated.
Session four never happens. Not for this project.
Two weeks later, they’re back. Fresh idea, fresh energy. New project. And the dopamine rush returns because the AI is genuinely brilliant at project initiation. The scaffold goes up fast. The thing looks real immediately. The loop starts again.
This user might be active for six months. They might have eight projects sitting in their workspace. They’ve shipped zero of them.
Here’s the brutal part: some platforms would call this retained user a success. Monthly actives. Healthy session counts. Good time-on-platform. Every metric looks fine until you look at the one metric that actually matters: did they ever ship anything?
Why the loop exists (this is the part nobody talks about)
Vibe coding dramatically lowers the startup cost of a new project. That’s the whole pitch. Zero-to-working-app in an hour. And it delivers on that promise. The early sessions on any project are genuinely productive. Fast feedback. Visual progress. The kind of tight build loop that feels like flow.
But lowering the startup cost also changes the economic calculus when things get hard.
When a project hits a wall, the user faces a choice. Fight through the complexity or start fresh. In traditional development, starting fresh is painful enough that fighting through is usually worth it. You’ve been in this codebase for weeks. You know where everything lives. Walking away means sunk cost you cant recover.
In vibe coding, starting fresh costs almost nothing. New project. Clean slate. Same dopamine hit you got on day one. The relative effort of “start fresh with a better idea” vs “debug this weird routing issue in a codebase I only half understand” tips sharply toward starting fresh.
And here’s the part that makes it worse: the AI actively enables this. It’s genuinely great at project initiation. It’s worse at project completion. The AI can scaffold a working app in 30 minutes. It struggles to debug a gnarly state management problem in an app it didn’t fully architect. So the user’s experience of the AI degrades exactly when they need it most, right as complexity peaks, which makes “start over with a better prompt” feel even more rational.
The loop isnt a user failure. It’s a product design problem. The platform optimized for the magical first-session experience and left users on their own to cross the finish line.
^ vibe coding founders discovering that session counts and project completion rates are telling completely different stories
What the loop looks like in conversation data
If you’re tracking conversation-level data on your platform, the build-abandon pattern has a very specific signature. You dont need to infer it from session counts. It’s readable directly from the session data.
The pre-abandonment conversation. In the final two or three sessions before a project gets abandoned, you’ll see a specific pattern emerge. Error loops, where the user pastes the same error message (or slight variants of it) across multiple turns. User messages get shorter as the session progresses, which is the opposite of what you see in productive sessions. Session ends without a resolution marker, the “it worked!” or “great, let me try that” that shows up in healthy sessions. Instead the conversation just… stops.
The abandonment event itself. A new project gets created without the previous project being marked done or deployed. This is your clearest signal. Not always a hard close, sometimes the user creates a new conversation context, sometimes they start a new repo, sometimes you can tell from the conversation topic shifting entirely from the previous project. However you define it, you can see it.
The return pattern. When the user comes back after 10-14 days of silence, they don’t go back to the old project. They start fresh. The new project is often similar in scope to the one they abandoned. Sometimes it’s almost identical, just described slightly differently. Same problem, cleaner starting point.
Across the platforms and agent products we track at Agnost, the pre-abandonment pattern is one of the more consistent behavioral signatures in the data. Rising error loop rate, declining session productivity score, and a session that ends mid-problem are three signals that, together, reliably precede project abandonment. Each one on its own is noisy. All three in sequence is a flag.
The 3 places you can actually break the loop
The build-abandon loop has three natural intervention points. Most platforms intervene at zero of them.
Intervention 1: Catch complexity before the wall.
The error loop doesnt come out of nowhere. There’s a period where session productivity is declining before the errors get bad. Messages are getting longer as the user tries to provide more context. The AI is asking clarifying questions it didnt need to ask before. Response length is increasing but resolution rate is dropping.
This is the moment to surface something proactively. A lightweight “your project is getting more complex, here are some resources” nudge, a “let me help you think through the architecture” prompt, or even just a flag that says “this looks like a good moment to review how the pieces fit together.” The goal isn’t to solve the problem for them, it’s to prevent the frustration peak that makes starting over feel rational.
Intervention 2: The re-engagement window after abandonment.
User’s session ended mid-error. Three days of silence. This is your re-engagement moment. Before they start a new project, reach out. “Your [project name] is about 60% of the way there. Here’s what was blocking you in the last session and one way to think about it.”
The specificity matters here. A generic “come back to your project!” push notification does nothing. But a message that shows you know exactly where they got stuck, and have something useful to say about it, that converts. The user already knows what the problem was. What they need is evidence that there’s a path through it that doesnt require three more hours of debugging.
Intervention 3: The new-project deflection.
This is the highest-leverage intervention and almost nobody does it. When a user starts a new project within 7-14 days of abandoning a previous one, don’t just enable the new start. Acknowledge the pattern.
“Before we build this, you were 60% through [old project] when you last stopped. Want 15 minutes to get it over the finish line? Here’s the specific issue we left on.”
Some users will say no and start the new project anyway. That’s fine. But a meaningful percentage will take the path back, especially if you make that path feel shorter than starting over. The users who say yes and finish the old project will have a categorically different relationship with your platform going forward. Shipping something changes everything.
Why shipping one thing matters more than you think
The “I shipped something” moment is the deepest form of activation in the vibe coding category. It’s not just a milestone, it’s a belief update. The user now has evidence that this platform can take them from idea to deployed app. That belief is what drives long-term retention, referrals, and expansion revenue.
Users who successfully ship a project on a vibe coding platform have dramatically higher LTV than users who dont. This isnt surprising in hindsight but it’s not what most platforms are optimizing for. They’re optimizing for session counts, for time-on-platform, for features generated. Project completion rate is almost never a primary KPI in the companies I’ve talked to.
It should be.
Here’s how to think about it: every user who goes through the full build-abandon loop without ever shipping becomes a cautionary story in their own head. “I tried that AI coding thing, spent three weeks on it, never actually finished anything.” They might stick around for months because the dopamine of new projects keeps pulling them back. But their NPS is low. They dont recommend the platform. They’re not evangelists. They’re habitual restarters.
The user who ships one thing is different. They have a real artifact in the world. They told people about it. They’re already thinking about what to build next, but this time with a track record. That user compounds.
Platforms that help users cross the finish line instead of just enabling the loop become genuinely indispensable. Platforms that enable the loop become a hobby that users eventually feel vaguely guilty about, the way people feel about half-finished courses on Udemy.
The metric: project completion rate
Define it simply: a project moves from “in active development” (regular sessions over a 7-14 day window) to “deployed or published” state. You dont need a sophisticated definition. You need a consistent one.
Track completion rate by cohort and by user segment. The numbers will be uncomfortable at first, they usually are. But this is the metric that separates platforms that are genuinely making users more capable from platforms that are selling the feeling of being productive.
The conversation signals that drive this metric are readable in your data right now. Error loop rate trends across a project’s session history. Session productivity scores in the late stages of a project vs early stages. Whether users return to a project after a gap or start a new one. Cross-project session continuity, which is just a fancy way of asking whether users are making progress or running in place.
At Agnost, we track these signals as part of the user behavior layer we built for AI-native products. The goal is always the same: give teams visibility into what’s actually happening in user sessions before the outcome shows up in a lagging metric. For vibe coding platforms, that means surfacing the build-abandon pattern in real time, not three months later when you’re trying to explain a flat retention curve to your investors.
If you’re building a vibe coding platform and your project completion rate is sitting in the single digits, that’s the number to move. Everything else is a proxy.
^ your user, when your platform helped them actually ship the thing instead of abandoning it for project number six
Wrapping it up
The build-abandon loop is a structural problem, not a user problem. The same property that makes vibe coding platforms so compelling, the near-zero cost of starting a new project, is what makes finishing one feel optional. You built a product that’s maximally good at the first 20% of every project and left users to figure out the last 80% on their own.
The fix isn’t to make starting new projects harder. It’s to make finishing existing ones feel achievable, specifically at the moments when abandonment risk is highest. That takes visibility into what’s happening in sessions, not just whether sessions are happening.
Project completion rate is the metric that tells you whether your platform is producing builders or just building habits. Right now, most platforms don’t even track it.
That’s the gap worth closing.
^ you, after instrumenting project completion rate and finally having a clear picture of what’s actually happening between “project created” and “deployed app”
TL;DR: Vibe coding users dont churn, they loop. Start a project, hit a wall, abandon it, start a new one. Repeat for months. Your DAU looks fine. Your project completion rate is in the gutter. The fix: three intervention points (complexity detection, post-abandonment re-engagement, new-project deflection) and one primary metric to track (project completion rate by cohort). Users who ship something have fundamentally different LTV than users who don’t. Build toward the ship, not just the start.
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