The Grenbaud case is no accident. It is a warning sign.
In March 2026, Grenbaud launched a social network live, built almost entirely using artificial intelligence. It cost around 40 euros, was developed in a matter of days and was online within a few hours, complete with real users, real data and real interactions. Then what always happens when something ‘seems to work’ but wasn’t actually built to do so happens: it takes very little to take control of the platform.
No sophisticated attack, no genius hacker, no advanced technique. Just a system that couldn’t defend itself.
The point, however, isn’t what happened. It’s why it happened. Because today, building software has become incredibly easy, but understanding what you’re building is anything but. And this is precisely where the problem increasingly arises: when speed and simplicity are mistaken for control.
What really happened: the Baudr timeline
On 14 March 2026, Grenbaud launched Baudr live on Twitch in front of thousands of viewers. The idea is simple: a social network to connect its community through profiles, interests and private messages, presented as an experiment built almost entirely with AI, without a structured technical team and with minimal investment. A case that has also been analysed in various technical insights on the subject (Baudr case study).
The hype is immediate, and users start signing up, creating profiles and using the platform as if it were already a fully-fledged product. And this is precisely where the critical shift occurs: from experiment to production, without the infrastructure actually being ready.
Within a few hours, a publicly accessible page emerges: /admin. No exploit or advanced technique is needed; simply typing in a URL is enough, causing a data breach that exposed the data of thousands of users, as documented in the initial technical analyses. From there, everything accelerates. Anyone can access administrative functions, delete accounts, read data and send messages by impersonating other users. This is because the controls were purely front-end and therefore easily bypassed by anyone with even a basic understanding of how to open the browser’s DevTools. We are talking about one of the most common vulnerabilities in web applications when security is not managed on the server side.
Thousands of profiles are compromised, including Grenbaud’s own.
Users began receiving messages they had never written and seeing data altered or exposed, whilst the platform remained online and patches arrived too late. In the days that followed, the site was updated several times and finally taken offline.
The key takeaway is that this was not a brilliant attack, but rather a product launched without the bare minimum foundations to withstand even the simplest test.
It wasn’t a sophisticated attack.
It was a basic mistake: a system put online without any server-side checks.
The problem isn’t AI. It’s how you use it.
At this point, it’s easy to jump to the wrong conclusion and blame artificial intelligence. To say that it generates insecure code, that it isn’t ready, that it can’t be used to build real products. But that’s not the point. In fact, I am convinced – even if it goes against my interests as a developer – that AI has done something extraordinary: it has drastically lowered the barrier to entry. Today, you can go from an idea to a working prototype in a matter of hours, write code, build interfaces and connect services without having years of experience behind you. And that is precisely what makes it so powerful.
The problem lies in the illusion it creates: the illusion that ‘if it runs, then it’s ready’.
But a real product isn’t just code that runs. It’s architecture, permissions management, access control, separation of frontend and backend, data management, error handling, security. All things that don’t come to light until it’s too late, but which determine whether a product holds up or not.
In Baudr’s case, the problem wasn’t using AI to build quickly. It was deploying that code into production without going through that invisible yet crucial phase known as engineering. Because there’s a world of difference between “it works on my browser” and “it can be used by thousands of people”.
Vibe coding is a powerful tool for exploring, prototyping and validating ideas. But when you use it to build systems that handle real users and data without understanding what’s going on underneath, you’re delegating critical decisions to something that isn’t designed to take on that responsibility. AI doesn’t make mistakes: it does exactly what it’s told.
The problem is that nobody has really checked the result.
Viewing Baudr solely as a technical issue means missing the point. The vulnerability is the symptom, not the cause. The error lies further upstream.
The first mistake is treating a real product as a side project. Baudr was public, with users and personal data. It was no longer an experiment. The moment someone registers, enters information and interacts with the platform, you are no longer ‘testing something’ but have responsibilities towards users – in this case, indeed towards your audience of supporters.
The second mistake is confusing MVP with a fragile product. In recent years, the term MVP has been simplified to the point of becoming almost an excuse: let’s launch something incomplete, as it’s only a first version. But an MVP is not a broken product. It is stripped down, but it must have solid foundations. Authentication, permissions management and data protection are not optional extras; they are part of the package.
The third mistake is to overlook critical skills. You don’t need a huge team to build an MVP, but there are certain areas you simply cannot skip: backend development, security, access management, and in many cases, the legal aspects relating to data. Ignoring them doesn’t make them any less important; it just makes them more dangerous.
And this isn’t just about Baudr. More and more founders are building products with incredibly powerful tools, quickly arriving at something that ‘works’, but completely skipping the stage where they figure out whether that something is actually ready to be used by others. The problem isn’t speed. The problem is not knowing what you’re sacrificing whilst chasing it.
The dangerous myth: ‘developers are no longer needed’
In recent months, an increasingly widespread narrative has been emerging: with AI, anyone can build anything. Developers are no longer needed, deep expertise is no longer needed; all you need is an idea and the ability to write the right prompt.
It’s a fascinating narrative, because it contains a grain of truth. Today, you can indeed build much faster than before, put a product online, connect services, and create interfaces and logic without starting from scratch.
The problem is what is left out.
Building something does not mean understanding it. And above all, it does not mean knowing how to handle it when things go wrong. AI helps you produce output, but it does not automatically give you the mental framework needed to design complex systems, anticipate risks and understand where things might break. And this is where the misunderstanding arises: the ability to generate code is confused with the ability to engineer.
In the Baudr case, the stack was modern and seemingly functional. What was missing was the understanding needed to make it reliable. To ensure that the foundations were laid in concrete rather than sand.
To say that ‘developers are no longer needed’ is like saying that, because building has become easier, engineers are no longer needed. You may be able to build something quickly, but you have no guarantee that it will hold up when the first real stress comes along.
AI does not replace professionals. It amplifies them. And it makes the difference between those who know what they’re doing and those who don’t even more evident.
Vibe coding: when it makes sense (and when it doesn’t)
At this point, it’s important to clarify one thing: vibe coding isn’t the problem. In fact, it’s one of the most interesting tools we have today. It allows you to explore ideas quickly, test interfaces and put together prototypes in timescales that were unthinkable until recently. For a founder, especially in the early stages, it’s a huge asset.
If you’re validating an idea, building a landing page or testing a user flow, using AI to generate code is often the right choice. It saves you time, cuts costs and gets you quickly to the point that really matters: figuring out whether what you’re building makes sense.
The problem arises when the scale changes. When you move from prototype to product, from internal testing to real users, from simulation to real data. At that point, the rules change completely, even if from the outside it looks like exactly the same product.
This is where many founders go wrong. Because what they see is something that works, whilst what they don’t see is everything that’s missing underneath: server-side controls, access management, data protection, error handling, security logic. All things that don’t come to light until someone actually tries to break the system.
Vibe coding works brilliantly as long as you’re building something that can afford to break. But the moment what you’re building involves other people, that margin disappears. You’re no longer experimenting on your own; you’re making decisions that affect others.
That’s why the distinction isn’t between ‘AI yes’ or ‘AI no’. The distinction is between contexts where you can afford to be approximate and contexts where you can’t. Understanding when this shift occurs is probably one of the most important skills today.
AI doesn’t eliminate complexity.
It hides it. And that is the difference between a prototype and a product.
What you really need to learn if you’re building an MVP today
The Baudr case is extreme, but the pattern is much more common than it seems. If you’re building an MVP today, especially using AI or no-code tools, the point isn’t to slow down or complicate your life, but to understand where you can’t afford to simplify.
The first thing to clarify is simple: if there are users, it’s no longer a game. The moment someone registers, enters data and interacts with your product, you’re dealing with real responsibilities. It doesn’t matter whether you call it a beta, an experiment or an MVP; for those using it, it’s already a product. Similar applications without adequate controls can lead to various problems, including legal ones, as highlighted by several analyses on the subject.
The second is to understand that some things are not ‘features’, but foundations. Authentication, permissions management, data protection, server-side controls: these are not improvements to be added later; they are what allow the rest to exist without breaking. You may have fewer features, but those must be solid.
The third is to be honest about what you don’t know. AI allows you to get a long way on your own, but it doesn’t tell you when you’re entering areas that require specific expertise. And that’s precisely when you need to pause for a moment and work out whether it makes sense to carry on alone or whether it’s time to bring in someone who knows what they’re looking at.
Finally, perhaps the most important thing: speed and quality are not mutually exclusive, but neither are they independent of one another. You can move fast, but every choice has a cost. If you don’t see it straight away, it doesn’t mean it doesn’t exist; it just means you’ll pay for it later.
Building things today is easy. Building them well is another matter.
The Grenbaud case is not the story of an individual mistake. It is a snapshot of a much broader shift. Today, anyone can turn an idea into something that works in no time at all, and this is undoubtedly a huge step forward.
But this accessibility has a side effect: it makes complexity invisible.
The easier it is to get started, the easier it is to underestimate everything that comes next. The quicker you get to something that works, the easier it is to confuse ‘it works’ with ‘it’s ready’. And the more powerful the tools become, the more dangerous they are if used without awareness.
There is nothing wrong with wanting to build things yourself, with using AI, with experimenting or with moving quickly. In fact, that is exactly what allows many ideas to exist today. The point is knowing where this phase ends.
Because there always comes a point when you stop exploring and start actually building. A point when your product ceases to be just yours and starts to involve other people. And at that moment, the rules change, even if everything looks the same from the outside. AI allows you to move faster. Professionals are there to stop you from falling over when you really start running.
And the difference between the two, today, is everything.
