It’s late in the evening. The coffee next to my laptop has long since gone cold, but I smile with satisfaction. In just a few hours, I have created an entire web application from scratch without typing a single line of code traditionally.
No expensive development team, no months of programming – just me, my idea, and an AI coding tool. I explained what I wanted in natural language. Welcome to the world of VibeCoding. Sounds like a start-up fairy tale? Maybe it does. But before you put your entire development team on the street: it’s not quite that simple after all.
In this article, I take you on a journey through the new phenomenon of VibeCoding – programming via vibe, which means with AI support instead of classic code tapping. We’ll explore how AI tools such as Cursor, GitHub Copilot or no-code platforms enable founders to build software and SaaS services at lightning speed. We ask: Can start-ups truly get off the ground faster and more flexibly now, without armies of developers and multi-million dollar budgets?
Does this change the rules of the game in the start-up scene? Furthermore, as this is a legal blog, we will examine the legal risks hidden behind this tempting AI turbo. Who is liable if the magical AI suddenly makes a mistake? Who actually owns the code that wasn't written by a human hand? And as a founder, what do you ultimately have to inform your users about if the AI is pulling the strings in the background?
So, let's navigate through hype and reality, through euphoria and legal texts. This is a personal, trenchant stocktaking – full of enthusiasm, but also with the critical eye of an IT lawyer who asks himself every day: “What the hell’s next?”
From Code Servant to AI Tamer: The VibeCoding Revolution
Let's start with the term itself: VibeCoding. If you're wondering what that means – no, it has nothing to do with music or esoteric vibes. VibeCoding describes the current trend of no longer programming software manually line by line. Instead, it involves developing it almost exclusively with the help of AI systems or no-code platforms.
Instead of laboriously cramming syntax and setting every comma themselves, founders or developers simply tell the AI what the software should do in normal language. The AI – whether a specialized code assistant like Cursor or a generalist like ChatGPT – automatically translates these requests into executable code.
The Core Principle of VibeCoding: "I Describe, the Machine Codes"
This motto summarizes the essence of VibeCoding. Visual no-code construction kits follow a similar principle: you click and configure on an interface, and the code is created invisibly in the background. In both cases, traditional manual programming work is replaced by automation. This marks a true democratization of software development.
Suddenly, even non-computer scientists can create functional applications. Theoretically, tech founders now need fewer human resources to get their ideas up and running as a product. This is a small revolution in the industry.
Consider a few years ago: In 2015, if a start-up wanted to build an app or a web service, it almost inevitably needed a team of developers. Alternatively, it required a tech-savvy co-founder who would spend all night coding. These times are rapidly changing.
Today, a single motivated founder with a vision, a laptop, and an AI tool can build something presentable in a few days. The temptation is great: finally, you can realize your own ideas without being at the mercy of rare (and expensive) software developers.
Some start-up founders report with shining eyes that they have "clicked together" – or rather "chatted together" – an entire SaaS service within a weekend. For example, an entrepreneur recently described how he used ChatGPT and a handful of cloud services to create a functioning web app in under five hours. This included user login, database connection, a smart user interface, and integration of third-party providers like Stripe for payments. All of it was written by the AI, without him having to program it himself!
A few years ago, hiring a freelancer for such a project might have cost 50,000 euros and many weeks of work. Now, an AI assistant handles large parts of it in a fraction of the time, at a cost in the double-digit dollar range. This involves only a few prompting sessions and API calls. It makes you rub your eyes involuntarily.
I also find myself thinking about whether I shouldn't build my next small website at night with such an AI code editor, instead of asking a web developer for a quote. The promise of efficiency is simply too tempting. The AI becomes a "colleague" that never sleeps, doesn't talk back, and spits out code suggestions in seconds that would take a human hours.
Tools such as Cursor – an AI-supported code editor that practically thinks as a pair programmer – or GitHub Copilot have precisely this goal. They are designed to make developers at least twice as productive, automate routine work, and even generate complete code on demand. You simply type what a function should do, and the AI coder fills in the rest. It's like "autocompletion on steroids."
AI Construction Kits and Code-Writing Machines
The market is exploding with such tools. In addition to Copilot (Microsoft), there's Amazon CodeWhisperer, Tabnine, Replit Ghostwriter, and Codeium. They all promise to complete code, find bugs, search through dozens of files in a project, and suggest suitable changes.
Some tools go even further by attempting to compile entire applications based on a prompt. Platforms like Bolt.new, v0.dev, or Lovable.dev allow you to describe an app's functionality in continuous text. For example, you might say, "Build me a club membership management system with login, admin portal, diagrams for usage statistics, a Stripe payment module, and email dispatch via SendGrid..." The AI then quickly conjures up a working framework of code, UI, and database.
While this sounds like science fiction, it's becoming increasingly effective. Though such generated applications are often rudimentary or a little "jerky" – the AI only builds what you tell it, not necessarily what you actually meant – they are always good as a prototype or MVP.
Traditional no-code/low-code platforms are also evolving. Services like Bubble, Webflow, Adalo, and Wix are integrating more and more AI assistants. Bubble, for instance, is testing AI features that suggest formulas or workflows to users. Webflow uses AI to generate design suggestions or texts.
Then there are specialized offerings like the Durable AI Website Builder. This tool promises to create a complete small company website from a handful of keywords within 30 seconds, including suitable AI-generated texts and images. Creating a website by entering a single line, as if ordering a coffee to go, is already a reality for simple web applications.
All of this leads to an exciting question: Does today's start-up still need expensive developers? Or are programmers the new weavers of the industrial age, replaced by the automated looms of AI? Of course, this exaggeration is somewhat unfair. Good developers will by no means become superfluous overnight.
However, their job profile is changing. Coders are becoming controllers and architects. Instead of typing every line themselves, they orchestrate the AI, check its output, make corrections, and manage the overall structure. "It's evolution, not extinction," someone recently said regarding AI vs. humans. The developer becomes the AI trainer who lets the lions jump but holds the whip in the background, ensuring no one in the circus gets eaten.
Faster, Cheaper, More Competition with VibeCoding?
For founders and small teams, these developments are a blessing – at least at first glance. More flexible? Faster? Cheaper? Yes, yes, and yes! A one-person start-up can now undertake tasks that were once just wishful thinking. The time-to-market is shrinking dramatically: an idea today, a clickable prototype tomorrow, online the day after tomorrow, and collecting initial user feedback. What more could you want?
Changes to the concept? Don't panic. If large parts of the code have been generated, you can also pivot more quickly. You can pivot in a week instead of six months because the AI handles most of the conversion work. That sounds like a start-up turbo, a real disruption, even within the start-up scene.
One could even say that the barriers to entry are falling. Anyone who previously failed to implement their software idea due to a lack of programming skills or money for developers now has a chance. The playing field is becoming more even. Not only Ivy League graduates with tech co-founders can launch successful apps, but theoretically anyone with a good idea, some business acumen, and the ability to use AI tools.
This should make the start-up world more colorful and diverse. However (and this is where the realist in me comes through), easier accessibility also means increased competition. If I can implement an idea with a minimal budget, then others can copy it just as quickly and cheaply. Differentiation becomes more difficult.
In the past, superior technology or elaborate development may have provided a head start. Today, however, the actual idea, timing, distribution, and access to the customer matter more than ever. This is because pure implementation in code is hardly a limiting factor anymore. If anyone can create a passable clone of my product with AI support, I must either grow rapidly, establish my brand, or offer something that cannot be easily replicated. This could include special data, a strong community, or a patented innovation – but more on that later.
Furthermore, just because it is easier to produce software does not automatically mean that every new application will be successful. The quality and sustainability of AI-fast-built products are separate concerns. We could see a boom in new SaaS tools and apps, but also a boom in half-baked solutions that disappear as quickly as they appeared. "Fail fast, fail often" takes on a whole new meaning when projects can be launched (and fail) on the fly.
Hype vs. Reality: What AI Tools Cannot (Yet) Replace in Software Development
Before we all become overly euphoric, a little reality check is in order. Is everything truly as effortless with AI as it sounds? Experience shows that it is both yes and no. The great stories about weekend MVPs and 5-hour SaaS solutions are true, but they often conceal pitfalls in the details.
The AI handles a lot of routine work for you, yes – but you still have to think, analyze, and, most importantly, plan properly yourself. A developer friend of mine – now more of a prompt engineer – recently told me about his first foray into fully automated coding.
He had described in detail to ChatGPT (with GPT-4) the web app he wanted to build, including the tech stack: Node.js in the backend, React/Tailwind in the frontend, connection to AWS for file storage, Google login, and more. The result was quite impressive. The AI spit out code for the backend and frontend piece by piece and even explained how to set up the development environment. He dutifully copied everything, and the basic functions actually worked straight away. But the joy was short-lived.
As soon as he wanted to expand or change the system, he realized that the AI quickly forgets details of its own code during the chat process. It suddenly produced contradictory changes, undoing what it had built shortly before. When he tried to add a menu item, the AI assistant rewrote half the front end with different buttons and ugly layouts because it didn't "know" exactly what it should look like.
Every new feature became a game of chance: sometimes accurate, sometimes a shot in the foot. Additionally, ChatGPT naturally had no connection to the running development environment. If an error message appeared in the terminal (which often happened, e.g., due to an incorrect node version), my friend first had to laboriously copy the error text and explain to the AI what had happened. This "blind debugging from afar" routine cost him nerves and time.
In short, he couldn't do it without his own coding skills and manual intervention. In the end, he was exhausted – "even Dark Mode didn't save my tired eyes," he joked – and looked around in frustration for an alternative.
The alternative was a specialized AI app builder (the very Bolt.new I mentioned). The application actually delivered a basic framework for his idea in record time, with significantly less back-and-forth than the chatbot approach. Nevertheless, similar problems arose there too.
When the AI was supposed to change something, it liked to rewrite large parts of the app unnecessarily, destroying working components and diligently burning expensive compute tokens in the background. Every complex feature became a casino session – you pulled the lever ("Please AI, add feature X") and hoped that this time the right code symbols would line up without breaking anything else. A few $80 later, he had a running application (actually completed in under 5 hours), but my friend had mixed feelings: "Basically, I've created a new job for myself: I'm now a baby-sitter for an AI. She's been taught to code, but I have to keep cleaning up after her." It could hardly be said more aptly.
What do we learn from this? AI tools are powerful, but not magical. They are great accelerators, but they are not error-free and certainly not foolproof. Anyone who leaves a complex application completely to AI without a programming background is acting much like someone who lets a high-tech autopilot system take the wheel and thinks they can sleep in the back seat.
That may work 99 times, but the 100th time you end up in a ditch – or worse. Someone has to stay alert, take countermeasures, and intervene if necessary. In our case, this means that without a basic understanding of software architecture, logic, and quality assurance, things can get dicey. The AI does not put its own code through its paces. It delivers what probably sounds right. But is it actually robust, secure, and efficient? The "autopilot" lacks real judgment.
This blind trust can be fatal, especially for security-critical or business-critical applications. It may be acceptable to have security vulnerabilities or inefficient queries for a quick prototype. However, when it comes to going live with real user data, the fun stops. Errors that an AI introduces are no less dangerous or expensive just because an AI has built them.
On the contrary, they can be more treacherous because developers may be inclined to trust the machine code ("It'll be fine, the AI generated it that way"), even though they don't understand it 100%. This can create a false sense of security that comes at a high price.
To summarize, VibeCoding can make you incredibly productive and give small teams superpowers. But shortcuts come at a price. You save time and money initially, but you might have to pay twice later – either through extra debugging, architecture refactoring, or, in the worst case, through problems with customers and investors. And that brings us to the next, less enjoyable topic.
Legal Pitfalls: Liability, Ownership, and User Information in the Age of AI
I hope you'll forgive me for putting on my legal glasses now. After all, this blog is called ITMediaLaw, not TechCrunch. Despite my fascination with VibeCoding, as a lawyer, I am naturally keen to ask: What does the law say about this wild development? The answer: surprisingly little so far – but the risks are real and often underestimated. Let's look at the most important points to ensure this cool AI experiment doesn't lead to a rude awakening in court.
Liability: AI is No Good as a Scapegoat
Let's imagine your start-up has used AI to put together an online service that is very popular. Everything is great – until one day user data is lost due to a software error, or something worse happens. Perhaps your AI-generated FinTech algorithm calculates incorrect interest rates, causing a customer financial damage. Who is now responsible? You'll have guessed it: Not the AI.
It is difficult to sue or seek recourse from an AI; legally, it is simply a tool, nothing more. You are responsible as the person who used the tool and brought the faulty product onto the market. Period. There are clear principles for this under German law. If your product – in this case, software – causes damage to a third party, you are generally liable if you have not exercised the necessary care.
Section 823 of the German Civil Code, for example (tort), obliges anyone who negligently injures the property, life, health, etc., of another person to pay compensation. And don't think you can get out of this by saying, "But the AI messed it up, not me!" Such an excuse doesn't work, any more than a hammer manufacturer can say, "It's not my problem if the user hits his thumb with my hammer." If you use a product and want to make a profit with it, you also have to take responsibility if it goes wrong.
Now you might argue: "Well, software always has bugs. Can't I simply exclude liability in the contract, along the lines of: use at your own risk?" Unfortunately, the law on general terms and conditions puts a spanner in the works. Liability can only be restricted to a very limited extent in the general terms and conditions for customers, especially end consumers, but also in B2B contexts.
German law (e.g., Section 307 BGB) prohibits the complete exclusion of liability for simple negligence when it comes to material contractual obligations. Gross negligence or intentional acts can never be waived. In other words, even if you were to write in your terms of use "I accept no liability for any errors in my AI software," this would be invalid in almost all cases. This is particularly unacceptable for consumers and prohibited for personal injury or total failure anyway.
In case of doubt, you are also liable for simple, stupid errors in your software if these errors have serious consequences for the contractual partner and you had a duty to check the software sufficiently. Of course, not every small bug will immediately lead to compensation – a breach of the duty of care is required. But if, for example, there was no quality control at all, and a fatal error went undetected as a result, things look bleak. The law expects you to check AI-generated code as well, at least as reasonably as you can. There is no complete absolution just because "it was the AI."
Incidentally, if your software error causes someone to suffer damage (personal injury, damaged property), product liability would theoretically also come into play. However, purely digital products without a physical component have not previously been covered by product liability law. The EU recently decided to consider software as a "product" in the future, which is outlined in the New EU Product Liability Directive 2023: Extended liability for software, AI and digital products.
In the near future (as soon as the corresponding directive is transposed into German law), software manufacturers could therefore also be held liable under product liability. For example, if an AI-controlled system in a machine causes an accident. This affects platform providers as well as start-ups. It would then be even more difficult to avoid liability with contractual clauses, as there is no exemption from liability in product liability. In case of doubt, liability insurance will cover this, if you have one.
The bottom line here: Liability risk remains liability risk, whether with or without AI. Anyone who operates software must bear responsibility. VibeCoding does not release you from careful testing, quality assurance, and sensible error handling. It just suggests that you should perhaps do this even more intensively, because the code does not come from an experienced senior developer, but from a probabilistic text machine.
To put it bluntly: the AI has no liability, but you do. So act accordingly. If you're hoping to hold the AI tool vendor liable when their code generator screws up: good luck. Most providers have extensive disclaimers in their terms of use. At most, they assume responsibility for the technical availability of their service, but not for indirect damages, loss of profit, etc., and often only up to the amount of the fees you have paid.
Additionally, the legal situation suggests that a platform operator is not responsible for every mischief a user makes with their tool. Just as a car manufacturer is not liable if you drive your car into a wall because you blindly followed the navigation system. Only if the platform itself has a fault (e.g., a systematic bug in the no-code engine that makes all generated apps unsafe) can the manufacturer be held liable. But even then, providers try to protect themselves by all means.
In short: If in doubt, you're on your own when it rains. No "daddy Microsoft" or "uncle OpenAI" will come to help you if you find yourself in court or in front of the customer because of an AI coding error.
Copyright & Co.: The Intellectual Owner a Ghost?
Even more exciting – and many people are not even aware of it – is the question of who actually owns the code that the AI writes. More precisely: Does this code enjoy copyright protection, and if so, for whom? Or, to put it another way: can someone simply steal your AI code without any legal consequences?
The answer is a little unsettling: much of what an AI creates could not be protected by copyright due to a lack of human creativity. Under German copyright law, a work must be a "personal intellectual creation" of a human being (Section 2 (2) UrhG) to be considered a work. In principle, the law treats code in the same way as text – software can also be protected by copyright if it is original enough.
However, the catch is that fully AI-generated code lacks the personal intellectual contribution of a human being. The AI does not "create" anything in a creative sense; it only combines probabilities and existing patterns from its training data. So if I, as the founder, tell the AI "Write me function X," and it spits out 100 lines of code, I have not designed this code creatively myself.
My input (the description) may well have come from me, but in most cases, this is probably not enough to be considered a co-author of the specific code. After all, I have not usually dictated to the AI word for word what it should write, but only described the goal. The concrete implementation – the choice of words, algorithms, etc. – comes algorithmically from the depths of the model. For more on this, see the article on Ownership of Software – Who Actually Owns the Code?
The consequence: This output could be considered "ownerless" under copyright law because there is no human author. Neither the AI (which cannot be the author by law), nor the user (whose contribution was too abstract), nor the operator of the AI (who built the model but did not write the specific line) would therefore have a classic copyright to it. This is uncharted legal territory, but the trend is moving in this direction.
There are parallels: In patent law, for example (DABUS case, 2024), the Federal Court of Justice has made it clear that an inventor in a patent must always be a human being; a machine cannot be considered an inventor. In copyright law, the creator principle has always applied – the author is the person who created the work. A fully autonomous AI creation falls through.
What does this mean in practical terms for a startup? First, it seems to be a relief: if your AI code is not protected, you can use it freely without asking anyone for permission – because you are not infringing anyone else's copyright (there is none). However, this is only half the truth and has a nasty downside: if your code is unprotected, anyone else can use it.
You then have no monopoly like an author who can say "I wrote this, don't copy it!" If a competitor gets hold of your source code (whether legally or illegally), they could copy it, modify it, and use it commercially. You would not be able to take legal action against them for copyright infringement because there is no copyright that could be infringed. Your software would be in the public domain, so to speak.
Imagine the puzzled faces in an investor meeting when the classic due diligence question "What IP do you own?" is answered with: "Um, actually none – most of our code is in the public domain because it was generated by an AI." I'm exaggerating a little to make things clearer, but this is exactly the kind of constellation that venture capital lawyers are currently discussing. The IP valuation of a start-up changes considerably if no original copyrights can be asserted for the software.
The "product" then consists more of a collection of ideas, business processes, perhaps trademark rights – but the code itself, normally a valuable asset, is difficult to protect legally. In such cases, trade secrets will be used: keeping the source code under lock and key so that nobody gets it. Secrecy instead of copyright protection, so to speak. However, organizational measures must be taken to ensure this (access restrictions, NDAs, etc.), otherwise it is not considered a secret within the meaning of the GeschGehG.
There are still a few creative approaches to solving the IP problem. For example, founders could specifically program critical parts themselves to at least have copyrights for them – a "human coat of paint" on the AI facade, so to speak. Or they can apply for patents (where possible) if a technical invention exists. However, here a human inventor must be named, which can be tricky if the idea actually originated from AI suggestions. You can also try to build exclusive data sets or AI models that are not available to others to secure a competitive advantage that goes beyond pure code.
In any case, investors will be watching very closely. Any unresolved copyright or licensing issue in the code is a potential dealbreaker or at least value detractor in the valuation. The nice speed advantage of VibeCoding can then be bought at a high price later on. This happens if the investor says: "Great software, but legally a shaky deal – we'd rather pay 20% less and demand that you do an IP scan and some reprogramming before signing the contract." This highlights the importance of thorough legal due diligence, especially when using VibeCoding and no-code platforms.
Speaking of licensing issues, another copyright aspect is even more controversial: It's about the AI training data. AI coding models, such as GitHub Copilot or the generic models behind it, have "learned" vast amounts of existing code from the Internet. This most likely includes code fragments that are protected by copyright and/or under open-source licenses.
It has already happened that Copilot has spit out code to users that matched a publicly available code snippet almost 1:1, including specific comments. With a bit of bad luck, such a snippet comes from a GPL-licensed library. If you now use these lines in your proprietary project, you are violating the license if you do not comply with the conditions, such as making the source code publicly available under GPL. Suddenly, you have one foot in copyright infringement.
You can't justify it by saying that you "didn't know" or that the AI gave it to you – legally, this is irrelevant. You have used someone else's code, that's it. If the copyright holder notices (or a diligent warning lawyer), you could face injunctive relief and claims for damages. To be on the safe side, experts are already painting devils on the wall, for example, in the form of a possible "co-pilot troll." Someone could deliberately place their own code under a strict license, hope that AI systems will catch it during training and output it later, and then systematically issue warnings to infringers who have adopted this AI output without checking it. Whether this actually happens remains to be seen – but the possibility exists. For further reading, an article about Open Source in Software Development: Legal Principles and Practice could be relevant here.
For start-ups, this means they have to be extremely careful about what the AI delivers. A thorough code review is not only mandatory from a quality perspective but also from a compliance perspective. There are tools that check the source code against known open-source repositories (code similarity scan) to find suspicious matches. You should use such tools, especially for larger blocks of code that do not appear completely generic.
Sometimes you can also recognize it by stylistic things: Suddenly a completely different comment scheme appears in the code, or very specific variable names that don't match the rest of the structure. These could be clues that "copying" has been done here, albeit by the AI as a ghostwriter. In short: Trust is good, control is better, otherwise, the foreign code can become a time bomb.
What Do Users Need to Know? Transparency and Data Protection
One last legal point: Do you actually have to inform your users that AI is working in the background for your website/SaaS or has even written the code? Do you need a warning along the lines of "This software was created fully automatically, use at your own risk"? Rather no – at least not across the board. There is no general obligation to disclose how the software was created. The end user is primarily interested (and concerned) in how the software works, not who or what built it.
You also don't have to tell the user which programming language you used or whether your developers drink coffee or mate – just as little as whether the AI helped. However, there are scenarios in which transparency is indeed legally required. Namely, whenever the user himself interacts with an AI function or his data is processed by an AI, and this has consequences for him.
Example: Your SaaS uses an AI internally to analyze user input or make automated decisions (e.g., an AI evaluates creditworthiness, filters content, or makes personalized recommendations). Regulations such as the EU AI Act and existing data protection rules are likely to apply here in the future. Depending on the context, you will then have to disclose that AI is involved. The GDPR already stipulates that certain information rights exist in the case of automated individual decisions with legal effect (Art. 22 GDPR) – the user can request a human verifier, etc. Even if your service does not have such blatant automation, many users now expect a certain degree of honesty from a moral point of view. If they are talking to a chatbot that actually has GPT-4 in the background, you should at least not secretly pass it off as human.
Many companies therefore voluntarily use labels such as "AI-supported" or let the AI introduce itself as such so as not to gamble away trust. Data protection is another aspect: If you use external AI APIs (e.g., you send text or code to OpenAI to have something generated), users' personal data may flow to third-party providers. This must be stated in the privacy policy. And you need a legal basis for this, of course – usually, it will be contract execution. But make sure that your contracts with the AI service are data protection-compliant (keyword order processing, third-country transfer to the USA, etc.).
There could be obligations to provide information here. For example, if you use analytical AI, you could explain in the privacy policy that an AI system evaluates user behavior to improve the offering. So, please don't forget such transparency issues in the heat of the moment. For further details on data protection, consider Data leak in startup practice: GDPR reporting and damage limitation.
Last but not least: If your website displays content that has been generated by AI (e.g., blog posts, product descriptions, news articles), you do not have to explicitly label this as long as no one is misled or legal requirements (such as copyright notices) are violated. Nevertheless, many choose to at least have internal quality controls and, if necessary, indicate that content is AI-generated in order to create trust.
This would be a topic of its own, but remember: AI likes to hallucinate facts. If your marketing page is written by an AI, have it proofread. You don't want to inadvertently advertise with false promises or infringe licensing rights to generated images. This will also ultimately fall back on you. The general topic of Why text AI is not 100% reliable when it comes to contract drafting! also applies to other content generation.
Conclusion: VibeCoding – A Turbo for Founders, Not a Free Ride
VibeCoding is awesome. I commit to that statement. As a tech enthusiast, I find it absolutely fascinating what is possible today. The speed at which an idea can become a product has reached a new dimension. This marks the start of a new chapter for the start-up world: more experiments, more innovation, possibly also more competition – but overall an exciting dynamic.
If you're clever, you can get in on the action with the big players without a huge budget, at least concerning the development phase. The often-cited "gap" between those with money/developers and those with just ideas is closing somewhat. This is good for equal opportunities and brings a breath of fresh air.
But (and here comes the big "but," you expected it): A startup is not just code. And success is not only measured by how quickly you launch something on the market but also how sustainably it works, how much customers trust it, and how well you have risks under control. In all these aspects, AI brings speed but also uncertainty.
The proliferation of features in a very short space of time should not obscure the fact that quality assurance, legal hygiene, and strategic differentiation still require hard work. Perhaps even harder than before, because the bar is higher: When anyone can launch quickly, it is all the more important to be error-free and legally compliant to stand out from the crowd.
For developers, VibeCoding does not mean the end, but a change. Many developers will be able to work more productively and concentrate on more interesting problems, while the AI takes care of the monotonous parts. However, there may also be fewer developers needed for the same task. A single talented engineer with AI support could do the work of three average coders. This could lead to a shakeout: mediocre coders will have to work harder or specialize, and true experts will become all the more valuable as mentors of AI and architects of complex systems.
New roles will also emerge: prompt designers, AI quality managers, data curators – professions we didn't even know we needed until recently. Investors will take a closer look: "Do they have their AI under control?" will become a standard question. Startups that use AI for coding have to do their homework: proper documentation, license checks, backup plans. If you are naive, you are in for a rude awakening when questions such as "Can you prove that there is no protected third-party code in the product?" or "How do you ensure that your software is protected and unique if the AI generated it?" suddenly arise during due diligence. This topic is also covered in Legal preparation for the first investment round.
A unique situation so far: you may have to explain to the investor that the code is not protectable by copyright – and still convince them that the business model is viable. This is a challenge, but feasible with good preparation. Transparency and proactive measures are key here.
And what about the end users of our Vibe-coded marvels? Ideally, they shouldn't feel any of this, except that they get a functioning product. The best AI-supported apps are recognized by the fact that they are simply good – not by the AI stamp emblazoned somewhere on them. Nevertheless, as manufacturers, we have a responsibility to handle this new power ethically.
When AI makes decisions, no user should be discriminated against. When AI generates content, we should stick to the truth and not recklessly spread fake news. Regulation will take a closer look here in the future, but we can also act sensibly on our own initiative. Further insights into ethical considerations can be found in Ethical issues and liability risks in automated decision-making processes.
Final thought: Startups can fly faster today, but they have to be careful not to hurtle into the abyss without brakes. VibeCoding is like a jet engine on your scooter: you can go faster than ever – but without a helmet and good brakes, it's a suicide mission. The flexibility and speed are fantastic; indeed, it will probably turn the entire industry upside down.
In a few years' time, we may look back with amusement on the days when founders had to spend months coding a prototype. But some things won't change: if you want to build a company sustainably, you still need a plan, responsibility, and an eye for the big picture. AI can do a lot, but it doesn't relieve us of responsibility – neither technically, legally, nor morally.
So, dear founders: take advantage of the new opportunities! Be brave, try VibeCoding, automate as much as you can, and let your ideas fly. But stay vigilant. Test your AI code, do your legal homework, and educate your users fairly where necessary. Then AI is not your enemy, but your ally.
And to my colleagues in the legal sector: get ready, we are needed. While the developers may gain some free time, our to-do lists could get longer – with new contracts, new liability issues, and new consultancy projects relating to AI development. It certainly won't be boring.
VibeCoding – the vibe is real, the code (almost) writes itself, but in the end: trust is good, control is better. With this in mind, happy prompting and stay on the safe side!