How AI-assisted software development actually works
The idea is simple: instead of manually writing code, 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: describe what you want, by voice or in writing
Output: the AI generates the code
Debug: if it doesn't work, copy the error, paste it, and repeat until it miraculously resolves
The result? A project that grows quickly, but one you often don't fully understand. It's like assembling IKEA furniture blindfolded: you'll finish eventually, but don't ask what you actually did.
When AI can genuinely help (and it's not always)
That said, it would be naive to think this development mode is completely useless. On the contrary, there are situations where it can be a valuable ally:
Rapid prototyping
Got an idea and want to see if it holds 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's 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, allowing you to explore without the commitment of formal development. It's a great way to "get your hands dirty" and understand if it's worth diving deeper.
Repetitive task automation
Scripts to automate boring operations? AI can write them in seconds, saving you precious time.
In summary: 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 fully delegating development to AI
Now we come to the painful point. This approach has obvious limitations, especially when we're talking about serious projects designed to grow and last over time.
Maintainability: code becomes a nightmare
Code written without a deep understanding of the underlying logic is extremely difficult to maintain. Try modifying something after six months (or even just six days): you'll face a wall of incomprehensibility. And good luck explaining it to a colleague.
Scalability: impossible growth
A project born this way isn't designed to handle an increase in users, features, or complexity. It's like building a house of cards: one gust of wind and it collapses.
Loss of control: the "why" disappears
Without knowing why the code works a certain way, any future evolution becomes practically impossible. You lack the fundamental understanding that allows you to make strategic decisions.
Code quality: standards go out the window
AI doesn't have an overall vision of the project. It doesn't know your team's best practices, doesn't understand 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's guided by expertise. We use AI tools to speed up certain processes, but never to replace the deep understanding of what we're building. We're meticulous about doing things right, with precision, because we know that serious projects require solid foundations.
Artificial intelligence is a powerful tool, but that's exactly what it is: a tool. Not a shortcut, not a magic wand, not a developer substitute.
Conclusions: using AI with (human) intelligence
AI-assisted development makes sense for:
Rapid prototypes and experimental projects
Automation of repetitive tasks
Exploration of new technologies
It doesn't make sense for:
Products designed to grow and last over time
Complex projects requiring maintenance
Situations where code quality is fundamental
The real challenge isn't delegating everything to AI, but understanding when to use it and how to integrate it into a professional development process. Because building solid and scalable digital products requires expertise, vision, and that deep understanding that, for now, only humans can have.
And if you need someone who actually knows what they're doing (and who might help you fix that AI-generated prototype that's now become unmanageable), we're here. With our skills, our irony, and our obsession with work well done.