Hey devs đ,
With AI tools now able to build full apps from a single prompt, many are asking, is it still worth learning to code? In this post, I want to share why the foundations of coding still matter, even in this AI-powered era. Letâs go beyond just vibe coding and talk about building real skills that last.
With AI capable of building apps on demand, is learning to code still worth it? Absolutely, and hereâs why the basics matter now more than ever.
Weâre living in extraordinary times. Today, even someone with no technical background can use tools like ChatGPT, Claude, or Cursor to generate a full-stack application, deploy it, and proudly call themselves a âdeveloperâ, all without writing a single line of code. This trend, letâs call it âvibe codingâ, is real, and itâs reshaping our perception of what it means to program.
But it raises an important question in tech communities everywhere:
If AI can write the code, why should I bother learning to code myself?
The Temptation of Vibe Coding
I've seen it over and over again:
- A beginner gets inspired by an app idea
- They ask AI to build it
- They copy and paste the code
- It works, they deploy it, and feel accomplished
- Then it breaks, and they hit a wall
That initial high is powerful, youâve âbuiltâ something! But whatâs really happened is a false sense of mastery. This isnât real coding, itâs just sophisticated prompt crafting.
Why Core Skills Still Matter (Even More Now)
1. You Canât Fix What You Donât Understand
When your AI-built React app suddenly crashes or behaves unpredictably, what do you do? If you donât understand JavaScript basics, state management, or component lifecycles, youâre left guessing, and often spinning in circles with conflicting AI answers.
2. AI Enhances What You Already Know
The developers who benefit most from AI tools are the ones who already know their stuff. They use AI to:
- Speed up repetitive coding tasks
- Explore new libraries and APIs
- Write and refine tests
- Clean up and optimize existing code
They donât use AI instead of thinking, they use it to think faster.
3. The Real Work Begins After the Build
Only a small part of software development is initial creation. The bulk of the effort goes into:
- Debugging and maintenance
- Feature updates
- Performance tuning
- Integrating systems
- Adapting to new requirements
AI can assist, but without foundational knowledge, you wonât be able to make sound decisions or scale effectively.
How to Learn Coding in 2025
Choose One Language and Master It
Whether itâs Python, JavaScript, or Go, pick one and dive deep:
- Learn data types, loops, and functions
- Understand how memory works
- Get comfortable with async operations
- Build small projects without AI initially
Why it matters: Knowing one language well makes you better equipped to judge AI output and tailor it to your goals.
Focus on Problem-Solving, Not Just Syntax
Being able to write a for loop is fine, but knowing why and when to use one is more important. Practice:
- Deconstructing complex problems
- Studying algorithms and data structures
- Reading error messages
- Debugging logically and patiently
Use AI as a Mentor, Not a Shortcut
Once youâve got the basics down, AI becomes a great learning partner:
- âWhy is this code slow?â
- âIs there a cleaner way to write this?â
- âWhat does this error mean?â
- âCan you review this for security risks?â
Build Projects, The Hard Way First
Before handing everything to AI:
- Create a simplified version yourself
- Understand every single line
- Then let AI assist with enhancements or improvements
- Compare both versions and learn from the differences
The Skills That Truly Matter
Critical Thinking: Can you identify problems in AI-generated code? Can you weigh options and pick the best one for your situation?
System Design: Can you structure an app thoughtfully and prepare it to scale?
Debugging: When things break (and they will), can you figure out why and fix it effectively?
Communication: Can you explain your code? Collaborate well with teammates? Document clearly?
A Realistic Learning Roadmap
Months 1â3: Foundations
- Choose a language
- Build simple tools (e.g., calculator, to-do app)
- Use a debugger
- Learn basic data structures
Months 4â6: Intermediate Growth
- Dive into APIs and databases
- Start learning to write tests
- Build a full project yourself
- Use AI for specific, well-understood tasks
Months 7â12: Going Deeper
- Study architecture and system design
- Learn security fundamentals
- Contribute to open source
- Use AI to increase speed, not replace thinking
The Honest Truth
Yes, learning to code is still challenging. It takes effort and time. AI hasnât changed that, itâs just shifted where the difficulty lies.
The developers who will excel in the AI era wonât just be expert prompt engineers. Theyâll be problem-solvers, system thinkers, and effective communicators, who use AI to amplify their impact, not avoid the hard parts.
For Seasoned Developers: Guide the Next Wave
If youâre experienced, nowâs the time to mentor. New developers are learning in a world where AI is at their fingertips, but they still need guidance. Help them avoid shallow learning. Share your debugging stories, code reviews, and architectural insights.
In Summary
Should you learn to code in 2025?
Yes, more than ever. But learn it the right way.
AI is an incredible asset once youâve mastered the fundamentals. It can make you faster and more effective, but it wonât replace deep understanding, creative thinking, or the ability to debug real-world issues.
The future belongs to developers who can both leverage AI and think independently. Donât just vibe through code, build the foundation that will make you unstoppable in the AI-driven world.
Have you seen âvibe codingâ in your community? Whatâs your experience with AI tools in programming? Letâs talk in the comments.
Top comments (1)
Just like you said AI tools like ChatGPT are most resourceful and creative when you're smartly guiding it; I have experienced it! Imagine a powerful prompt that carries the power of a real developer đ», the AI simply turns to powerful buddy. When AI powers are unlocked đ is during the post-learning stage. For developers, AI in this age is the greatest asset you could have. For non developers who just want to build with the best shortcut and no hard-work, this age makes it appealing at the surface level. However the ones on top are critical problem solvers and real engineers. For example anyone could build a similar app to Facebook, with AI's help or not; however you couldn't keep up with debugging, updating, critical problem solving and continuous feature implementation, without being creative and tech inclined; AI doesn't help this process in any way đ.
I love your detailed guide đ.