Saed Verse

AI is replacing developers.
If you're learning to code right now, that sentence probably hit you harder than anything else.

You open your feed and see AI generating full apps in seconds.
Then you see endless roadmaps: HTML, CSS, JavaScript, React, backend, databases…

And suddenly, a very real question shows up:
“Why am I spending months learning something AI can do in 10 seconds?”

I’ve had that exact thought.

Seeing AI generate code instantly can make everything you’re learning feel… questionable.
And if it feels confusing now, it’s even worse when you’re just starting out.

But after using AI seriously, something became very clear:

AI is not making coding pointless.
It’s exposing who actually understands what they’re doing.

Let’s break down 7 common lies about AI and coding and what actually matters now 👇

Lie #1: There’s no point learning to code anymore

It feels true.

You ask AI for a login form, a landing page, even a small app and it delivers instantly.

So why learn?

Because writing code ≠ building software.

AI can generate syntax.
But it doesn’t understand:

your users
your product goals
edge cases
what happens when things break (and they always do)

Real-world software is messy:

requirements change
users behave unpredictably
systems fail in unexpected ways

That’s where developers matter.

👉 Learning to code isn’t pointless.
👉 Learning to think like a developer is more valuable than ever.

Lie #2: You don’t need fundamentals anymore

This one sounds smart… and that’s why it’s dangerous.

“Why learn HTML if AI can write it?”
“Why learn JavaScript if AI can generate it?”
“Why learn databases if AI explains everything?”

Here’s the problem:

If you skip fundamentals, you don’t become faster you become dependent.

You’ll get answers, but you won’t know:

if the code is correct
if it’s secure
if it’s scalable
or if it’s a complete mess

And AI will sound confident either way.

Fundamentals give you judgment.
They help you say:

“This doesn’t make sense.”
“This will break later.”
“There’s a simpler way.”

👉 Without fundamentals, AI leads you.
👉 With fundamentals, you lead AI.

Lie #3: Prompting is the new coding

Yes prompting is useful.

But no it’s not a replacement.

A “good prompt” isn’t magic.
It’s just clear thinking written down.

And clear thinking comes from understanding.

If you don’t understand what you’re building, your prompts will be vague:

“Build me an app like Airbnb… but simpler.”

(Which is already suspicious nothing about that is simple.)

AI will generate something impressive-looking…
…but it will break in multiple places.

And now you’re stuck debugging code you don’t understand.

👉 Prompting is a multiplier.
👉 But you still need something to multiply.

Lie #4: AI will replace all junior developers

Let’s be honest: the market has changed.

Junior roles are more competitive
Expectations are higher
AI has reduced repetitive coding work

But here’s the truth:

AI isn’t replacing juniors.
It’s replacing low-context work.

The kind of work that looks like:

copying tutorials without understanding
following steps without thinking
building only when guided line-by-line

That kind of learning was already fragile.

AI is just exposing it faster.

But juniors who can:

build small projects
explain their decisions
debug issues
ask good questions
use AI intelligently

👉 Those developers are still incredibly valuable.

So the real question is not:
“Will AI replace juniors?”

It’s:
“What kind of junior are you becoming?”

Lie #5: If AI gives me the answer, I learned it

This is one of the sneakiest traps.

AI makes learning feel productive:

You ask a question
You get a clear answer
You understand it while reading

And it feels like learning.

But then you open a blank file…
…and your brain disappears.

Because:

Understanding ≠ ability.

Real learning happens when you can:

solve problems without guidance
recall concepts
apply them in new situations

👉 AI removes friction.
👉 But some friction is necessary to learn.

Use AI better:

Ask why something works
Ask what would break it
Request simpler examples
Try solving similar problems yourself

👉 Don’t just consume answers.
👉 Train your thinking.

Lie #6: You need to learn everything now

This is where overwhelm explodes.

Now it feels like you must learn:

React, Next.js, TypeScript, Docker, AI tools, databases, automation.

And suddenly, you’re not learning…

You’re just collecting anxiety.

Here’s the truth:

Beginners don’t need everything.
They need a strong base.

Focus on:

building simple pages
styling them
adding interactivity
debugging issues
explaining your code

That’s still the game.

👉 Trends change.
👉 Fundamentals don’t.

If you chase hype, you’ll keep switching directions…
…and stay stuck as a beginner.

Lie #7: Real developers don’t use AI

This is the opposite extreme and it’s just as wrong.

Developers have always used tools:

documentation
search engines
libraries
frameworks
other people’s code

AI is just the next tool.

The issue isn’t using AI.

The issue is using it to avoid thinking.

Use AI to:

explain concepts
review your code
debug issues
compare approaches

But not to:

blindly copy
skip understanding
replace your reasoning

👉 If AI does all the thinking, you stop growing.

So… should you still learn to code?

Yes. But not the old way.

Don’t:

memorize syntax blindly
follow tutorials passively
copy projects without understanding
paste AI code and hope for the best

Do:

learn fundamentals deeply
build small projects
break things and fix them
think through problems
use AI as a mentor not a crutch
The real opportunity

The future doesn’t belong to:

people who ignore AI
or people who blindly trust it

It belongs to people who:

think clearly
adapt quickly
and use tools without switching their brain off

If you’re just starting out, here’s the takeaway:

👉 You’re not late.
👉 You’re just entering a different game.

And the rules are actually better
for people who focus on understanding.

If this helped you rethink your approach to learning coding,
share your thoughts below or pass it on to someone who needs it.

If you’re starting your coding journey and don’t know where to begin, here are a few websites that can genuinely help you learn step by step:

Platforms like freeCodeCamp, MDN Web Docs, and The Odin Project are great for building strong fundamentals. If you prefer structured courses, try Coursera or Udemy. And for practice, sites like LeetCode and Frontend Mentor can help you apply what you learn.

Don’t try to use everything at once. Pick one, stay consistent, and focus on understanding, not just finishing.

Save this for later and share it with someone who’s starting their coding journey 🚀

Ma ogtahay sababta saboolnimadaada? 🧏

13 Arrimood oo Keena Faqri una Diida Qofka Barako iyo Hanti:

1. Hurdo badan oo subaxii (caajisnimo xilliga barakada)

2. Ka tagista salaadda gaar ahaan salaadda subax

3. Beenta oo lagu dhaarto (dhaar been ah)

4. Sino (xadgudubka galmada ee xaaraanta ah)

5. Cunto la cuno iyada oo aan la oran "Bismillaah"

6. Gurigaga oo aad gasho adigoo aan "Bismillaah" dhihin

7. Dadka oo laga qoslo, lagu caayo, ama lagu yaso (ku mashquulid ceebahooda)

8. Amaanada oo la dayaco (aan lagu aaminin wax)

9. Waalidka oo aan loo ducayn (oo aan la wanaajin)

10. Qaraabada oo la jaro (xiriirka ehelka oo la gooyo)

11. Istighfaar la'aan (dembi-dhaaf weydiisasho la'aan)

12. Inaan qofku ku kalsoonayn llaahay (tawakal la'aan) ❕

13. Inaad is-hoosaysiiso adigoo mar walba dul taagan madaxda (boqorrada, madaxda... iwm) 😡

Akhristaha sharfanow hadaad halkan soo gaadhay sidaynu mar kale isku aragno adage qoraalka xaqiisa sii ❤️ hal mar ii taabo mahadsanid hunnno. 👍

I want to learn New language.

What is the best way to learn it?

Should I:

> Buy a course
> Watch youtube Tutorials
> Learn by doing
> Read Code
> Use websites like w3 schools

Would love to hear some opinions from you guys!

Exploring a Career in AI or Data? Start Here.

This guide breaks down 8 high-impact roles in AI & Data - showing you what skills, tools, and knowledge areas matter most for each:

1. Data Analyst – Turn raw data into business insights with stats and Python.
2. ML Engineer – Build predictive systems using modeling tools like Scikit-learn and TensorFlow.
3. AI Specialist – Apply AI in domains like healthcare, finance, and business intelligence.
4. AI Engineer – Use frameworks (PyTorch, Keras) to engineer production-ready AI systems.
5. Data Scientist – Combine stats, programming, and domain expertise for pattern discovery.
6. Agentic AI Expert – Design autonomous agents with LLMs, LangChain, and vector DBs.
7. AI Product Manager – Bridge business and technical strategy with knowledge of MLOps & LLMs.
8. AI Research Scientist – Dive into deep mathematical foundations and push the boundaries of AI.

Note: This is a quick snapshot, not an complete checklist.

Which of these roles are you aiming for in 2025? Let’s discuss!