Vibe Coding vs. No-Code: What We Ship to Production, What We Refuse to Ship
No-code and AI-assisted development save a considerable amount of time—up to a point. Where is the line, and how can you avoid paying the price later?
We use AI-powered development tools on a daily basis and deliver projects built on no-code platforms. We also regularly decide not to put certain things into production this way. This article explains where we draw the line—and why.
The Reality Behind the Speed Gains
This isn’t just a passing trend. When it comes to prototyping, the order-of-magnitude improvements reported by the industry are spectacular: a landing page that used to take two days to build can now be generated in an hour, and a minimum viable product that used to take three weeks is now up and running in three days. Our own figures point in the same direction for the early phases.
This gain has a strategic implication: exploration costs almost nothing. We can build three versions of an interface and have them tested, whereas before we would have presented just one in a slide deck. This is a significant step forward for the quality of product decisions.
Where the clock starts running backward
The problem isn’t code generation; it’s the unreviewed code that piles up. The industry has given it a name: “vibe debt.” It has three clearly identified sources—code produced in response to hundreds of prompts without ever being audited, the lack of automated testing, and vague requests that produce fragile code.
Two findings recur regularly in analyses published in 2026: approximately 45% of AI-generated code contains a security vulnerability, and code co-written with AI generates about 1.7 times more issues than code written by humans alone. This isn’t a reason to avoid it—it’s a reason to review it.
This debt has a perverse characteristic: it remains invisible as long as you’re adding features, and it suddenly reveals itself at the first serious bug or the first major structural change. At that point, no one on the team knows why the code was written that way.
Our dividing line
| Context | Approach | Why |
|---|---|---|
| Prototype, proof of concept, demonstration | Full-fledged AI generation, light review | The code will be discarded; only the learning matters |
| Internal tool, low criticality | No-code or AI-generated, review focused on data access | The cost of an incident is limited and internal |
| Showcase site, editorial content | AI-generated code + comprehensive review, non-regression testing | Reputation and SEO risks, limited attack surface |
| E-commerce platform, ERP, customer data | Human-centered architecture, AI-assisted, systematic review, testing | Money, personal data, availability: mistakes come at a high cost |
| Authentication, payment, access rights | Written and reviewed by a human, without exception | A vulnerability in this layer compromises everything else |
The five safeguards that make speed sustainable
- An architecture decided upon before code is generated. AI writes very well within a framework. It invents a different framework for each feature if one isn’t provided.
- Tests written at the same time as the code. This is the only safety net that allows you to modify code later on that you didn’t write yourself. Without tests, the initial speed comes at a full cost with the first iteration.
- A human review of everything related to data or money. Non-negotiable. That’s where vulnerabilities are concentrated.
- Automated security analysis in the pipeline. Dependencies, plaintext secrets, injection patterns. The cost is marginal; the coverage is immediate.
- A simple rule: no one merges code they can’t explain. That’s the shortest way to sum up everything above.
No-code and vibe coding are not the same thing
No-code locks you into the capabilities of a platform, but what it produces is maintained by the vendor and remains consistent. AI-assisted development produces real code, with no limits on expressiveness, but its consistency depends entirely on the team’s discipline.
The risk with the former is the glass ceiling: one day, the feature you need is impossible to implement, and you have to rebuild everything from scratch. The risk with the latter is erosion: the project remains feasible, but becomes increasingly costly to evolve.
Frequently Asked Questions
Can a no-code project be deployed into production?
Yes, and it’s often the right choice for internal tools, forms, and content sites. The key question to decide at the outset is the exit strategy: what happens if you need to migrate in two years? If the answer is “we don’t know,” the decision hasn’t been made yet.
How do you salvage a project that’s already in technical debt?
Through testing, not by rewriting. First, we cover the existing behavior, then we refactor under a safety net. A complete rewrite without testing will reproduce the same problems six months later.
Does this put developers out of work?
It shifts their work toward architecture, review, and validation—that is, toward the areas that truly determine a project’s success. The barrier is no longer typing speed; it’s discernment.
Conclusion
AI-assisted development is a remarkable accelerator for exploring, prototyping, and quickly delivering non-critical features. On a platform that handles money and customer data, speed without review is like taking out a variable-rate loan. We take this loan knowingly for prototypes, but never for the foundation.
Next up: where to start with AI automation. Do you have a rapidly developed project that’s starting to hit a snag? We regularly conduct recovery audits.