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Kudzai Murimi
Kudzai Murimi

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Learning to Code in the Age of AI: More Than Just the Vibes

#ai #machinelearning #discuss #learning

Hey devs 👋,
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. Let’s 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 here’s why the basics matter now more than ever.

We’re 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 “developer”, all without writing a single line of code. This trend, let’s call it “vibe coding”, is real, and it’s 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:

  1. A beginner gets inspired by an app idea
  2. They ask AI to build it
  3. They copy and paste the code
  4. It works, they deploy it, and feel accomplished
  5. Then it breaks, and they hit a wall

That initial high is powerful, you’ve “built” something! But what’s really happened is a false sense of mastery. This isn’t real coding, it’s just sophisticated prompt crafting.

Why Core Skills Still Matter (Even More Now)

1. You Can’t Fix What You Don’t Understand
When your AI-built React app suddenly crashes or behaves unpredictably, what do you do? If you don’t understand JavaScript basics, state management, or component lifecycles, you’re 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 don’t 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 won’t be able to make sound decisions or scale effectively.

How to Learn Coding in 2025

Choose One Language and Master It
Whether it’s 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 you’ve got the basics down, AI becomes a great learning partner:

  • “Why is this code slow?”
  • “Is there a cleaner way to write this?”
  • “What does this error mean?”
  • “Can you review this for security risks?”

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 1–3: Foundations

  • Choose a language
  • Build simple tools (e.g., calculator, to-do app)
  • Use a debugger
  • Learn basic data structures

Months 4–6: 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 7–12: 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 hasn’t changed that, it’s just shifted where the difficulty lies.

The developers who will excel in the AI era won’t just be expert prompt engineers. They’ll 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 you’re experienced, now’s 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 you’ve mastered the fundamentals. It can make you faster and more effective, but it won’t 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. Don’t just vibe through code, build the foundation that will make you unstoppable in the AI-driven world.

Have you seen “vibe coding” in your community? What’s your experience with AI tools in programming? Let’s talk in the comments.

Top comments (1)

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uzondu9 profile image
Uzondu • • Edited

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 💻, the AI simply turns to powerful buddy. When AI powers are unlocked 🔓 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 😔.
I love your detailed guide 😍.