The term “vibe coding” — coined by Andrej Karpathy in early 2025 to describe the practice of building software by prompting AI tools rather than writing code directly — has gone from niche shorthand to mainstream search term in a matter of months. Alongside it, “build an app with AI” and “AI app builder” have both broken out as queries, reflecting a genuine shift in how non-technical founders and product people are thinking about what’s now possible.
And in one sense, a lot of it is possible. Tools like Claude Code, Cursor, Lovable, and Bolt have made it genuinely easy to go from a rough idea to a working prototype in hours. That’s real. It’s not a gimmick. For demos, internal tools, and proof-of-concept builds, AI-assisted development has compressed timelines in ways that would have seemed implausible two years ago.
But there’s a gap between a prototype that works in a demo and an app that ships to real users and stays running. Most vibe-coded projects don’t make it across that gap — and understanding why is useful whether you’re a founder deciding how to start, or a team evaluating whether to hand off an AI-generated codebase to engineers.
The prototype problem
AI tools are excellent at generating plausible-looking code quickly. The problem is that “plausible-looking” and “production-ready” are different things. Generated code tends to handle the happy path well and edge cases poorly. It often lacks proper error handling, makes assumptions about data that break under real-world conditions, and produces architectures that work fine at zero scale and fall apart at any meaningful load.
None of this is a criticism of the tools — it’s the nature of what they’re optimised for. They’re optimised to produce code that looks right and runs on first attempt, not code that’s been thought through for maintainability, failure modes, and the unglamorous realities of production systems.
The security and compliance blind spot
Vibe-coded apps also tend to have security gaps that are invisible until they’re exploited. Input validation, authentication flows, data handling, API key exposure — these are the kinds of things an experienced engineer checks as a matter of habit and an AI code generator doesn’t prioritise unless specifically prompted. For apps handling personal data (which is most apps), or operating in regulated sectors like healthcare or finance, this isn’t a minor concern. It’s a hard blocker.
What professional engineers actually do with AI tools
This is the part that gets lost in the discourse: the most productive developers in 2025 and 2026 aren’t ignoring AI tools, they’re using them constantly. Claude Code, Cursor, and GitHub Copilot are part of the standard workflow at BuildApps and at most serious development studios. The difference is that engineers use these tools to accelerate work they understand — generating boilerplate, drafting tests, exploring unfamiliar APIs — rather than to replace the judgment about what the code should do and how it should be structured.
The result is that AI-assisted professional development is genuinely faster. Not vibe- coding fast, but consistently faster in ways that compound: less time on repetitive implementation means more time on architecture, edge cases, and the decisions that determine whether a product scales. We cover how we use these tools on our AI-assisted development page.
The practical question for founders
If you’re a non-technical founder who’s been experimenting with vibe coding, the honest answer is: it depends on what you’re building and where you want to go with it.
For validating an idea, a vibe-coded prototype is often the right call. It’s fast, cheap, and lets you test assumptions before committing budget to a proper build. The mistake is treating that prototype as the foundation for a real product — carrying it forward with patches and prompts until it collapses under its own technical debt.
The pattern we see most often is founders who start with AI-generated prototypes, validate something worth building, and then bring in engineers to build the real thing properly — usually starting from scratch with the prototype as a reference rather than a codebase. That sequencing makes sense. What tends not to work is trying to stretch the prototype further than it was designed to go.
The surge in vibe coding searches reflects genuine excitement about what’s now possible for people who couldn’t previously build software. That excitement is justified. The products that actually reach and keep users are still the ones where someone made deliberate decisions about quality, reliability, and what the product is actually trying to do.