AI and Development: Mastering Invisible Traps

Introduction

Imagine a brilliant junior developer who writes code at the speed of light, but has no notion of architecture. That’s what GitHub Copilot, Claude, or ChatGPT can become if left unsupervised. And that’s exactly what’s happening in teams that let AI drive.

In 15 years of development and software architecture, I’ve seen many technological upheavals. But this one is different. For the first time, it’s not the tool that adapts to the developer: it’s the developer who must reinvent themselves.

AI Hasn’t Replaced Devs… It Changed the Rules

The good news? Statistics are clear: we’re not disappearing. The bad news? Our job no longer exists as before.

Some Telling Numbers

According to a study by MIT and GitHub (2023), Copilot increases productivity by +55% on programming tasks. Impressive, right? But wait for the rest.

McKinsey (2024) reveals that developers spend 40% less time writing code. However, they spend 40% more time in review and validation. It’s a complete redistribution of work time.

And here’s the number that should alarm you: 64% of AI-created bugs come from poor architectural decisions, according to a Stanford HAI study (2024). Not syntax errors. Not trivial oversights. Hasty architectural decisions.

The Paradox: Faster, But More Risks

If we’re heading straight into a wall, AI just gets there much faster.

This isn’t a joke. It’s the daily reality in teams that settle for telling their AI assistant “do this for me.” The tool does its job. But nobody asked the question: “Should we really do this?”

The 8 Invisible Traps of AI-Generated Code

This is where it gets concrete. Here are the drifts I regularly observe in clients and open-source projects.

1️⃣ Over-Engineering: The Solution That’s 3 Times Too Heavy

The trap: You ask for a simple user list. The AI produces a Repository + Factory pattern + an asynchronous queue for sorting + Redis caching. To list 10 users.

Why it’s serious: Each abstraction layer adds complexity. Complexity is technical debt. And this debt is paid with time we don’t have.

By analogy: It’s like ordering a sandwich from the baker and receiving a complete artisan bakery with wood-fired oven imported from Italy.

2️⃣ AI Proxy Coding: When Dev Becomes Operator

The trap: “Make me a REST API.” → Code generated → Copy-paste → Commit. Without reading anything.

The developer becomes a proxy between the client and the AI. They no longer code. They coordinate. And they don’t understand what’s really happening.

Why it’s serious: When the bug arrives (and it always arrives), the dev can’t fix it. They don’t understand the code. They ask the AI. It proposes a correction that creates two other bugs. It’s a vicious cycle.

3️⃣ Feature Creep: The AI That Adds “Just One Thing”

The trap: “I added statistics on top, it was no big deal!” The AI just multiplied your controller’s complexity by 5. But it was optional.

It’s a smile trap: each generated feature seems so easy to add that we forget they stack up.

Why it’s serious: At the end of the project, we have 30% of features we never used. But 100% of maintenance.

4️⃣ Hallucination: Code That Doesn’t Exist

The trap: The AI generates code that calls LaravelMagic::transform($data). Except this method doesn’t exist. Not in Laravel, not anywhere. The AI invented it.

It’s a hallucination: the AI fabricates certainties with confidence. It invents libraries, methods, patterns it believes it knows.

Why it’s serious: You discover this during testing. Or worse, in production.

5️⃣ Vanity Patterns: Patterns That Shine, But For Nothing

The trap: CQRS, Event Sourcing, Hexagonal Architecture… The AI loves sophisticated patterns. Even for basic CRUD.

By analogy: It’s like wearing a James Bond suit to shop at the supermarket. It’s beautiful, it’s classy… it’s completely off-topic.

Why it’s serious: Each pattern adds learning surface for the team. And for what? To show off skills? To make it complicated to maintain?

6️⃣ Ghost Dependencies: The Silent Explosion of Packages

The trap: npm install → 1 dependency → which requires 50 others → which require 50 others. And in the end, you have 500 packages to make a simple API call.

The AI generates code with imports without checking if there’s better. Result: your node_modules weighs 2 GB and contains 5 critical security vulnerabilities.

Why it’s serious: More dependencies = more attack surface. More updates to do. More compatibilities to manage.

7️⃣ Context Collapse: When AI Loses the Thread

The trap: You generate 200 functions with the AI, and from the 150th, it forgets the conventions you established. Names become inconsistent. Patterns change. Flows no longer align.

The AI loses the project’s global context.

Why it’s serious: You have a codebase where each part seems to come from different parallel universes.

8️⃣ AI-Induced Technical Debt: The Unpaid Bill

The trap: “We’ll refactor later.” With each generation, you postpone complexity. And since AI makes adding features very easy, we always generate more instead of consolidating.

Why it’s serious: Technically, you have a time bomb. Humanly, you have a team tired of maintaining code they don’t understand.

The common point of all these traps?

The AI has no product goal. It generates code. Period. It’s up to the developer to decide if this code should exist.

And this is where your value begins.

Developer Value Shifts… Up the Board 🧠

Here’s the secret managers haven’t understood yet: AI automates the “how”, not the “why”.

A Harvard Business Review study (2024) analyzed the best developers working with AI. They have one thing in common: they’re not the ones who code the fastest. They’re the ones who ask the best questions.

The Job Evolves

The best AI-assisted developers are those who:

• Ask the best questions before asking code to exist
• Challenge provided code instead of accepting it passively
• Simplify instead of complexify (it’s now your superpower)
• Maintain product vision when AI proposes detours

AI Makes Development More Human Than Ever

Ironically, code automation forces us to be more human. Because judgment, prioritization, overall vision… that’s not code. It’s technical leadership.

And that, AI will never do for you.

How to Master AI Instead of Suffering It

Here’s the concrete checklist I apply with my clients and in my personal projects.

🎯 Refocus AI on User Need

Before generating a line of code, answer this question: “What user problem does this solve?”

An AI without product context generates code that’s technically relevant, but commercially useless.

Concrete action: Write your prompt starting with the problem, not the solution.

Example:

• ❌ Bad: “Generate me a REST API for users”
• ✅ Good: “I need to allow users to update their profile in less than 500ms. Generate an optimized API for that.”

✂️ Reduce Complexity to Minimum Viable

Ask the AI for a simple solution before a complete solution.

Concrete action: Start with the main use case. Forget edge cases for now.

Example prompt:

Just generate the logic to display a list of articles.
Forget filters, pagination, caching for now.

Seems counter-intuitive? Think again. 80% of real cases settle for the basic version. The complicated 20% can wait.

🔍 Review Generated Code Like a Junior’s

Ask yourself these questions:

• Why is this pattern used?
• Where is the potential security flaw?
• How many dependencies could have been avoided?
• Who will maintain this in 2 years?

If you can’t explain the code to a junior, you haven’t understood it. And that’s a warning sign.

🧪 Test Systematically (Automated If Possible)

AI often generates code that seems right but isn’t.

Concrete action: Write tests BEFORE or AT THE SAME TIME as you validate generated code. Tests quickly reveal hallucinations.

Example test:

<?php
// Before validating generated code, test it
class UserServiceTest extends TestCase
{
    public function testUpdateUserProfile()
    {
        // This test will quickly reveal if the AI hallucinated
        $result = (new UserService())->update(user: $user, data: $data);
        $this->assertTrue($result);
    }
}

🚫 Ban Dubious Dependencies

When the AI proposes a library you don’t know:

1. Verify it really exists (hallucination)
2. Check its popularity (< 100 stars = caution)
3. Check its last update date (> 1 year = risky)
4. Ask yourself: could I code this without this lib?

If the answer is yes, code it without. A dependency we avoid is an attack surface we close.

🧭 Maintain Product & Architecture Consistency

Establish rules once, then enforce them.

Concrete action: Create a simple architecture document (one page). Give it to the AI in context.

Example prompt:

Here are our naming conventions and architecture.
Generate code respecting this [document]

✍️ Document Decisions, Not Just Code

Why you chose this pattern. Why you rejected that solution. Why this dependency is acceptable.

Because in 6 months, someone (maybe you) will read this code and wonder: “Why complicate this?”

Decision documentation saves project lives.

What AI Will NEVER Do For You

It can propose. But it can’t prioritize.

It can generate. But it can’t assume.

It can code. But it can’t carry a vision.

Conclusion: Tomorrow, Devs Will Be More Essential Than Ever

AI changed the game. It’s not the first revolution we’re going through (remember the transition from assembly to C, from C to web, from web to cloud). But it’s the fastest.

Here’s what I’d like you to remember:

• Your ability to say no to bad ideas becomes your superpower
• Your ability to make things simple when everything can be complex becomes your competitive advantage
• Your ability to think beyond code and see business impact becomes your reason to exist

AI offers us incredible speed. But we’re the ones who keep the course.

And clearly, that’s an incredible opportunity.

Article published on December 10, 2025 by Nicolas Dabène - Expert in AI development and software architecture with 15+ years of experience