Coding in the AI Era: How to Adapt Your Working Methods in 2025
In my development practice for over 15 years, I’ve lived through several technological revolutions. But the one of artificial intelligence surpasses everything I’ve known. 84% of developers use or plan to use AI tools, according to latest studies. The AI code assistant market is exploding, going from $5.5 billion in 2024 to a forecast of $47.3 billion by 2034.
Yet, a troubling paradox emerges: if AI can generate up to 46% of code in some projects, why do 66% of developers judge this code “almost correct, but not quite”? And above all, why do 45% of them spend more time debugging than before?
The answer holds in one word: methodology.
Introduction
Artificial intelligence radically transforms how we conceive software development. But attention: this revolution doesn’t simply consist of replacing our IDE with ChatGPT. It requires a complete overhaul of our working methods.
Imagine a carpenter who would suddenly receive power tools after years of manual work. If they use them as simply faster versions of their old tools, they’ll miss their potential and might even hurt themselves. AI in development is the same: poorly used, it creates more problems than it solves.
In this article, I share lessons learned from my practical experiences and those of many developers. Because unlike marketing promises, there’s no miracle solution - only processes to build and refine.
The New Development Landscape
Explosive Growth… and Its Challenges
Numbers speak for themselves:
- 1.5 million active users on GitHub Copilot
- 46% of code generated automatically in enabled files
- But also 66% of “almost correct” code according to developers
This situation reminds me of Internet’s beginnings in the 90s. Everyone knew it was revolutionary, but few truly mastered the stakes. Today, we’re living the same phenomenon with AI.
Junior/Senior Divide: An Unexpected Phenomenon
A surprising finding emerges from my observations: senior developers massively adopt AI (33% use it for over half their code), while only 13% of juniors dare take this step.
Why this disparity? Experience. A senior immediately knows when something “sounds wrong” in generated code. A junior can spend hours on a subtle bug introduced by AI.
More troubling still: juniors’ traditional training ground disappears. Those small repetitive tasks that forged reflexes? AI automates them instantly. It’s as if we removed elementary school while keeping university.
The Three Pillars of 2025 Skills
Facing this revolution, I’ve identified three essential pillars for any developer:
1. Timeless Fundamentals
Algorithms, data structures, computational thinking… These bases paradoxically become more important with AI. Why? Because they allow understanding and criticizing what AI produces.
2. AI Orchestration and Criticism
The developer becomes conductor. It’s no longer about coding, but:
- Clearly expressing intentions
- Dialoguing with AI
- Testing and validating results
- Criticizing and improving
3. Human Skills
Communication, collaboration, ethics, UX design… What AI can’t replace becomes your competitive advantage.
The PARR Methodology: Plan → Act → Review → Repeat
After many tests, I developed a structured approach I call PARR:
Plan: Engage AI in pure discussion mode. No code, just reflection. Build a detailed plan together.
Act: Request execution, but stay vigilant. At the slightest problem, stop - back to Plan.
Review: Analyze code with the same rigor as if it came from a colleague. Sometimes more.
Repeat: Iterate integrating learnings.
This method transforms AI from a simple code generator into a true development partner.
Critical Pitfalls (And How to Avoid Them)
Over-Reliance: When AI Becomes a Crutch
Symptom: Inability to solve a problem without AI assistance. Remedy: Alternate projects with and without AI. Maintain your problem-solving “muscles”.
Black Box Mentality: Accepting Blindly
Symptom: “It works, that’s enough.” Remedy: Always ask AI to explain its reasoning. Understand before accepting.
Vibe Coding: Coding “By Feel”
Symptom: Chaotic AI use without methodology. Remedy: Adopt a structured approach like PARR.
I personally experienced a case where an entire application coded with AI had major security flaws. AI had produced functional code, but vulnerable. Without critical review, the project could have been compromised.
Differentiated Strategies: Junior vs Senior
For Junior Developers
- Review line by line all generated code
- Decompose into tasks of maximum 30 minutes
- Document extensively each AI block
- Plan before prompting
AI can become your most patient mentor, but only if you constantly question it.
For Senior Developers
- Architectural focus during review
- Iterative approach: architecture → implementation → refactoring
- Business validation: AI doesn’t know your business context
Conclusion
AI integration in development represents an extraordinary opportunity, but it requires a rigorous and methodological approach. Success doesn’t depend on the quantity of AI code used, but on the quality of its integration into your processes.
This revolution amplifies good practices as much as bad ones. Developers who master this human-machine collaboration will transform AI from a simple code generator into a true development partner.
The future belongs to methodical developers, not necessarily the fastest.
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Article published on September 22, 2025 by Nicolas Dabène - PHP & PrestaShop Expert with 15+ years of experience in AI-assisted development
