Have you ever wondered whether artificial intelligence could do your job? Well, technically yes. But there is a "but" the size of a house.
In recent months there has been more and more talk of an approach to software development in which AI generates code in place of the programmer. No keyboard, no command lines, no coffee drunk in front of Stack Overflow at 3 in the morning. Just you talking (or typing) and the artificial intelligence executing.
Fantastic, right? Well, it depends.
Artificial intelligence and software development: how it really works
The idea is simple: instead of writing code by hand, you communicate with a generative AI tool (like ChatGPT, GitHub Copilot or similar) and ask it to create what you need. It generates, you copy and paste. If something goes wrong, you pass it the error and hope it fixes it.
The workflow becomes:
- Input: you describe what you want, aloud or in writing
- Output: the AI generates the code
- Debug: if it doesn't work, you copy the error, paste it, and repeat until it miraculously gets resolved
The result? A project that grows fast, but that you often don't fully understand. It is a bit like assembling IKEA furniture blindfolded: sooner or later you finish it, but don't ask what exactly you did.
When AI can really help (and not always)
That said, it would be naive to think this way of developing is completely useless. On the contrary, there are situations where it can be a valuable ally:
Rapid prototyping
Have an idea and want to see whether it stands up within a few hours? AI is perfect. You can create an MVP (Minimum Viable Product) or an automation script without investing days of development. It is ideal for throwaway projects or weekend experiments.
Creative experimentation
Want to test a new technology but don't have time to study the documentation? Artificial intelligence can give you a starting point, letting you explore without the commitment of formal development. It is a great way to "get your hands dirty" and see whether it is worth digging deeper.
Automating repetitive tasks
Scripts to automate boring operations? AI can write them in a few seconds, saving you precious time.
In short: it works well as long as the goal is speed and you don't need the project to survive more than a few weeks.
The risks of development delegated entirely to AI
Now to the sore point. This approach has obvious limits, especially when it comes to serious projects, meant to grow and last over time.
Maintainability: the code becomes a nightmare
Code written without a deep understanding of the underlying logic is extremely hard to maintain. Try changing something after six months (or even just six days): you will find yourself facing a wall of incomprehensibility. And good luck explaining it to a colleague.
Scalability: growth becomes impossible
A project born this way is not designed to withstand an increase in users, features or complexity. It is like building a house of cards: a gust of wind is enough to bring it down.
Loss of control: the "why" disappears
Without knowing why the code works the way it does, any future evolution becomes practically impossible. You lack the fundamental understanding that lets you make strategic decisions.
Code quality: standards go out the window
AI has no overview of the project. It doesn't know your team's best practices, doesn't know the architecture you have in mind, and often generates redundant or inefficient code.
Our vision: technology with awareness
At Quinck, we believe in the power of technology when it is guided by expertise. We use AI tools to speed up some processes, but never to replace a deep understanding of what we are building. We are obsessive about doing things well, with precision, because we know serious projects need solid foundations.
Artificial intelligence is a powerful tool, but that is exactly what it is: a tool. Not a shortcut, not a magic wand, not a substitute for the developer.
Conclusions: using AI with (human) intelligence
AI-assisted development makes sense for:
- Rapid prototypes and experimental projects
- Automating repetitive tasks
- Exploring new technologies
It does not make sense for:
- Products meant to grow and last over time
- Complex projects that require maintenance
- Situations where code quality is critical
The real challenge is not delegating everything to AI, but understanding when to use it and how to integrate it into a professional development process. Because building solid, scalable digital products takes expertise, vision and that deep understanding which, for now, only humans can have.
And if you need someone who really knows what they are doing (and who might help you fix that AI-made prototype that has now become unmanageable), we are here. With our skills, our irony and our obsession with work well done.
