Building an app or a website with artificial intelligence without writing code can seem convenient, but it hides pitfalls that can cost you dearly. In recent years several platforms have emerged that promise to turn a simple text description into a working website or application, with no need to know how to program. Lovable is one of the best known, alongside other similar tools built on generative artificial intelligence. The idea is appealing: you write what you want to achieve, and within minutes you have a product ready to show. But behind this convenience lie concrete risks, especially when these tools are used for real projects, with real data and real users.
What Lovable Is and Why It's So Popular
Lovable belongs to a category of platforms built around what's known as "vibe coding": you describe the desired result in words, and an artificial intelligence model writes the code for you. It's a tool built for speed: entrepreneurs, startups, and professionals who want to test an idea without hiring a development team. The advantage is obvious: you go from an idea to a working prototype in a very short time, with upfront costs far lower than traditional development. The problem is that this speed has a price, one that is often discovered only after something has already gone wrong.
The Risks of Vibe Coding
The most serious risk concerns data protection. Automatically generated applications can contain errors in access management: information that should be private and visible only to the account owner instead remains accessible to anyone who knows the right address. This isn't a theoretical scenario: in recent years, several documented cases have come to light of apps built with these tools publicly exposing sensitive data, such as emails, passwords, or personal information belonging to thousands of users, simply because no one had manually checked how permissions were configured. The reason is simple: an artificial intelligence model generates code based on statistical patterns, not on a genuine understanding of your specific project's security context. It can work perfectly on the surface and still have an open back door.
Another problem with vibe coding lies precisely in one of these platforms' main selling points: not requiring technical skills. If you can't read the generated code, you can't verify whether it was written well, whether it follows good security practices, or whether it contains hidden flaws. It's a bit like signing a contract written in a language you don't understand: you can trust that everything is fine, but you have no way of noticing if it isn't, until it's too late.
When you build a project entirely inside an environment like Lovable, you become dependent on that platform. If the service changes its pricing terms, alters the features available, or, in the worst case, shuts down, your project risks getting stuck or having to be rebuilt from scratch. This kind of dependency, often called "lock-in," is a risk that needs careful evaluation, especially if the project is meant to grow into a real business rather than remain just a test.
A tool designed to quickly generate a prototype isn't necessarily suited to supporting an application with thousands of active users. Growth brings different needs: traffic management, performance optimization, complex customizations, integrations with other systems. Many applications born from these tools, once they start truly succeeding, need to be partly or entirely rewritten by experienced developers, adding costs and time on top of what has already been spent.
The Legal Risks Behind These Applications
An often underestimated aspect is the protection of personal data: names, emails, payments, health information. If your app collects even just an email address, GDPR applies in Europe, and you, as the project owner, are the one legally responsible in case of privacy violations, regardless of whether the code was written by a human or generated by artificial intelligence. In the event of a data breach, you may have to notify the relevant data protection authority within 72 hours and, in certain cases, notify the affected users as well. For the most serious violations, GDPR fines can reach up to 20 million euros or 4% of global annual turnover, whichever is higher.
If a malfunction in the app causes harm to a user, such as data loss, financial damage, or the exposure of sensitive information, that user can sue you, as the owner of the service, for compensation. They cannot go directly after Lovable or similar tools, because it's the site owner who is the legal interface with the end customer: the law looks at who provides the service to end users, not at who (or what) wrote the code.
Two more often overlooked points add to this. The first concerns the intellectual property of the generated code: it isn't always clear who holds the rights, and if the model was also trained on open source code under restrictive licenses, you could end up commercially using code that, in theory, you aren't authorized to use. The second concerns regulated sectors, such as healthcare, payments, or services aimed at minors, where specific regulations apply on top of general privacy rules, often stricter ones.
In short, using artificial intelligence to build the app doesn't shift legal responsibility elsewhere: it leaves it entirely on your shoulders.
Should You Avoid Lovable and Similar Tools?
Tools like Lovable represent a real shift in how applications and websites are built: they make it possible to bring an idea to life extremely quickly, even without technical skills. But this speed doesn't eliminate the complexity of building software that is secure, reliable, and legally compliant: it simply pushes that complexity to a later moment, often when it's already too late to easily fix the mistakes.
The simplest rule to follow is this: use these tools to validate an idea, but before putting it in front of real users with real data, have the work reviewed, both technically and legally, by people with the expertise to spot the problems that an artificial intelligence model, however sophisticated, still can't see on its own.
At M's Works we build applications with security in mind from the first line of code, not bolted on as a patch once someone notices the gap (maybe a security researcher, maybe someone with worse intentions). A quick prototype is fine for validating an idea, but if your project needs to hold up under real data and real users, the difference between a lock-in that forces you to rebuild everything from scratch and an architecture that grows with you comes down to who can actually read the code, not who just generated it. If you're about to launch an AI-built app and want to know what's really under the hood before someone else finds out for you, get in touch.