There is no doubt that vibe coding is a powerful new tool. However, as with any tool, you can use it best if you thoroughly understand its strengths and its risks. Using AI-driven platforms like Lovable and Bolt to build apps by describing your desired features can make it faster, easier, and more accessible to create software.
Like no-code platforms, vibe coding tools allow even non-developers to create their own software. But AI can't do everything. It has blind spots, security vulnerabilities, and risks that you need to be aware of if you're going to try this novel technique. That goes double if you're planning on launching any of your AI-built software to the public or using it at work.
To use this tool well, you need to know the best uses for vibe coding and where it's not the right tool for the job. Here are some of the biggest risks and drawbacks.
1. Security vulnerabilities
The most prominent and concerning risk with vibe-coded software is the security vulnerabilities commonly included in the code these platforms produce. AI models learn from vast repositories of public code, including insecure or outdated patterns. As one security expert put it, “By definition, Copilot is an average of all developers' shared work. Unfortunately, all the security failings added to all known public codebases are part of the corpus on which it bases its suggestions.”
As a result, they can often suggest code with known vulnerabilities. Common types of security flaws observed in AI-generated snippets include the addition of malicious code, SQL injection, insecure file handling, improper authentication/authorization, and insecure file handling.
Since many of the people using vibe coding are not experienced developers, there's a good chance they'll miss some of the security problems that have entered their software and won't be able to fix them before going live. In practice, this means most of them will need to hire developers to inspect their code and fix errors, somewhat defeating the purpose of a “no code” AI development approach.
Security vulnerabilities are less of a risk if you're creating something that doesn't use a lot of private data, such as a landing page, but will be a serious problem for anyone wanting to vibe code an app or business tool that actively uses private data.`
2. Lack of maintainability and scalability
AI-generated code from vibe coding can be hard to maintain or scale as a project grows. The code often works just well enough to pass initial tests, but it tends to be brittle and poorly organized under the hood. Developers working on a vibe-coded project later are likely to find code with inconsistent structure, minimal comments, and ad-hoc logic. There is also a lack of proper documentation. This makes understanding or extending the software difficult at best.
Codebases like these accrue technical debt quickly. AI can introduce inefficient or overly complex solutions without considering long-term design. Because the AI doesn't have a thorough understanding of system architecture, it can produce inconsistent naming, coding styles, or logic flows, making the codebase harder to navigate and maintain.
As a result, scaling applications created with vibe coding tools can be challenging. Adding new features or handling more users can be difficult or impossible since the underlying system design might not support them. You may need an expensive and time-consuming rewrite of the whole app in order to continue using it over time.
3. Difficulties with debugging and troubleshooting
The process of inspecting, debugging, and troubleshooting problems for a vibe-coded app is described as extremely challenging, even for experienced developers.
Traditional development uses debugging (the process of inspecting and understanding code errors) to improve software. Debugging when vibe coding is much more challenging. Typically, when vibe coding, you will regenerate code until it works, rather than step through why it failed. This approach might work for simple syntax errors, but it breaks down with complex bugs.
When an AI generates code that behaves unexpectedly, developers can have trouble finding the source of the problem because they didn’t write the code themselves. The AI’s logic may be obscure or non-linear. One experienced engineer described debugging AI-created code at scale as “practically impossible”.
Platforms like Bolt and Lovable are launching new built-in troubleshooting aids, but these haven't fully solved the problem. For example, Bolt provides an “Attempt Fix” feature to auto-correct errors, but users report that fixes sometimes introduce new bugs or even remove working features inadvertently. One designer using Bolt explained she resorted to copying error messages into ChatGPT for advice, then carefully instructing the AI step-by-step on what to fix.
4. Limitations in custom logic and integration
Vibe coding platforms are generally optimized for the most common use cases and standard tech stacks. They may not be as effective when you need custom logic or integrations beyond their training or built-in features. For example, integrating with a niche third-party API or implementing a very domain-specific algorithm might be beyond what a vibe coding tool can handle reliably. The model has probably not seen that exact scenario in its training data.
Users have reported that AI coders get easily lost on uncommon or complex tasks. Trying to use undocumented libraries, handle challenging concurrency issues (e.g., timing, race conditions), or meet specific performance/scalability requirements may produce poor results.. You might get incorrect solutions or have to do extensive prompt refinements, which eat up time and money. Re-prompting these projects gets expensive fast.
Many vibe coding tools, including Lovable and Bolt, are essentially no-code or low-code platforms at their core. They only have the integrations and building blocks that their environment supports. This means if you need a feature outside that sandbox, you’re out of luck unless you write custom code.
Integration with certain databases, legacy systems, or less-common programming frameworks might also be limited or impossible. For straightforward apps, the AI can be incredibly effective, but for anything more bespoke or technically intensive, traditional development skills are still going to be needed.
5. Design overengineering and poor UX design
Vibe coding platforms are a truly groundbreaking tool for visual design and flexibility. However, they may not always produce functional, beautiful, or desired outcomes to your prompts.
Vibe coding works by replicating snippets of code that it's learned elsewhere. It doesn't have the expertise of a seasoned designer or the judgment of a human who has trained in UX/UI design. Software consultant Pete Hodgson quips that AI coding assistants have “somewhat questionable taste”. Vibe coding will create something that looks good extremely fast, but you might not be able to refine to the degree you would like to.
AI may also make design or architecture choices that are unnecessarily complex, often over-engineering a simple feature. It may sometimes introduce convoluted patterns or extra components that the user never asked for, just because it thinks they might be useful or impressive. The result is a more complicated app than necessary, potentially harder to understand for the user or slower to load, simply because the AI overbuilt the solution.
Flashy design doesn’t substitute for well-thought-out user experience design. Vibe coding can encourage a design-first, substance-second approach. You might get a pretty interface quickly, but ensuring the design serves the product’s goals without unnecessary complexity still requires human judgment.
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None of these risks or limitations necessarily means that you shouldn't use vibe coding. But understanding what they are and what it means for your projects will help you mitigate those risks. You’ll be better able to determine where vibe coding works for your needs and where it doesn't.
For some projects, vibe coding is the perfect solution. Traditional coding or proven no-code platforms may be a much better option.
