You’re probably tired of the AI noise flooding your feed. Everyone’s talking about how ChatGPT, Claude, Copilot, and their friends are replacing developers, automating everything from writing code to generating full apps in seconds. No bugs, no coffee breaks, no imposter syndrome, just 24/7 productivity.
So naturally, a big question is bubbling up:
Is It Worth Learning to Code in 2025?
If AI is already doing what developers do, and faster, why bother?
As a web agency that builds real products with real people, we think this is the wrong question. And recent studies from MIT and even Apple are starting to agree.
Let’s break down what the AI hype skips over, and why learning to code is not only still relevant in 2025, but possibly more important than ever.
AI Looks Great on the Surface, but It’s Not Actually Thinking
The AI evangelists will tell you that LLMs like GPT-4 can:
- Write code
- Refactor code
- Explain code
- Document code
- Review pull requests
And yes, in many cases, they can. But what they don’t do is think.
In a recent research summary by Apple, the tech giant quietly pointed out the limitations of generative models. Despite improvements in token prediction and multimodal capabilities, these models still lack genuine understanding, context retention, and critical reasoning.
They don’t solve problems. They output probable solutions. There’s a huge difference.
And here’s where things get even more interesting.
The MIT Brain Study That Should Be a Wake-Up Call
A group of MIT researchers ran a study dubbed the first “BrainScan on GPT users.” You can can read it here.
The experiment was simple: divide people into three groups and give them the same writing task.
- Group A used large language models (LLMs) like GPT.
- Group B used traditional tools like Google or Wikipedia.
- Group C used nothing, just their brain.
While the output was being written, participants wore EEG headsets to track brain activity. The essays were judged by both humans and natural language processing tools.
The results?
Using LLMs drastically reduced brain activity, especially in the areas responsible for:
- Problem-solving
- Memory recall
- Planning and structuring information
Even worse, over 80% of LLM users couldn’t remember quotes or ideas from their own essays just minutes after writing them.
Their neural connectivity was cut in half. And when they were asked to complete a task without AI assistance afterward, their performance dropped significantly, even lower than those who never used AI at all.
Let that sink in. The tool that’s supposed to make you more productive is actually making you cognitively weaker.
The Brain Is a Muscle: Use It or Lose It
This isn’t just academic trivia. If you’re a developer (or want to become one), the implications are huge.
Writing code isn’t just about syntax. It’s about:
- Understanding systems
- Holding abstract concepts in your mind
- Debugging logic
- Planning complex sequences of instructions
- Naming things (which is still one of the hardest problems in computer science)
These are precisely the cognitive functions AI is weakening when you rely on it too much.
At our agency, we’ve seen this firsthand. Developers who use Copilot too aggressively often write code that “works” but doesn’t scale, isn’t secure, and lacks architectural sense. The AI filled in the blanks, but the dev didn’t understand the full picture.
And that’s dangerous.
Why Learning to Code Is More Valuable Than Ever
If AI can write code but can’t think, and if using it too early makes you think less – then learning to code isn’t obsolete. It’s essential.
Because the developers who actually know how to reason will be the ones who can:
- Spot AI-generated bugs
- Understand when the LLM is hallucinating
- Build systems that scale and adapt
- Innovate beyond what autocomplete can offer
Think of AI as a calculator. It’s powerful, but only useful to people who already understand math.
And this is exactly what the MIT study concluded. In later sessions, researchers found that people with strong cognitive baselines, those who already had structured thinking habits, performed better with AI than those who didn’t.
So yes, AI can make you faster. But only if you already know what you’re doing.
Real Developers Build Real Products: Here’s How Enlivy Does It
Let’s get straight to the point.
At Enlivy, we craft full-stack web and mobile applications for startups, businesses, and teams who want solutions that work, not just flashy demos. Our preferred tech stack includes:
- PHP, Laravel, MySQL, Redis
- JavaScript, Vue.js, AngularJS, jQuery
- SCSS, WordPress + WooCommerce
- Flutter, React Native, Firebase
We’re open to using AI-powered coding assistants when it genuinely boosts productivity, but let’s be clear:
AI doesn’t replace skilled developers, it automates parts of their workflow.
Critical decisions like software architecture, UX design, database modeling, and project planning? Those still need sharp human minds. Real developers think, analyze, and solve problems – AI just can’t replicate that.
We’ve reviewed code from so-called “AI developers” relying heavily on large language models to scaffold apps. In over 80% of cases, the results are messy: duplicated logic, shallow component hierarchies, and fragile backends that break under real-world conditions.
If your clients are real people who demand reliability and long-term support, AI shortcuts won’t cut it. What you need are developers who understand your business and code thoughtfully.
Code Like a Human. Think Like a Developer.
So, is it still worth learning to code?
Absolutely.
In fact, it’s the best time to do it, because the future won’t belong to the people who blindly rely on AI. It will belong to the ones who understand how to think, how to structure logic, and how to build things that matter.
AI is here to stay. That’s not a debate.
But whether it becomes your assistant or your crutch is entirely up to you.
So invest in your cognitive skills. Learn to build. Stay curious. And when you’re ready to turn your ideas into actual products, we’re here to help.