Let’s clear up the biggest fear first.
AI didn’t kill programming.
It changed how we program — which means how we prepare has to change too.
A lot of people are saying, “Why learn to code when an LLM can write everything for you?”
That fear tells me they’re looking at programming through the wrong lens.
So let’s fix that.
AI changed how we learn, not whether we should learn
There’s a belief floating around:
“Why read anything when I can just ask an LLM?”
Sure — AI changed how we search for info.
But AI still hallucinates, and hallucinations don’t disappear just because the UI looks confident.
That means two things:
- We still need content, but written in a way AI can interpret and cite cleanly
- We still need people who understand enough to judge the output
Think of it like SEO shifts.
Every time Google updates its algorithm, the content format changes — not the need for content.
Same thing here: blog posts won’t die, they’ll just become more direct, more experience-driven, and easier for AI to surface.
Programming isn’t dying — the barriers are
The old thinking says:
“Broaden your knowledge. Do more courses. Learn everything.”
That’s outdated.
People aren’t struggling because they don’t know enough.
They’re struggling because they’re learning the wrong way.
The real shift is this:
You don’t need to master the details. You need the concepts.
Variables, conditions, loops, arrays, components, APIs.
Enough to understand what AI is doing — not enough to manually do it all yourself.
AI handles the bulk.
Your job is to know:
- Where AI fails
- What information it needs
- How to prompt it to fix the mistake
That’s modern programming.
The #1 skill: Prompt engineering for debugging
People expect AI to generate perfect code.
It won’t.
The real skill is reading the output, reading its reasoning, and tightening your prompt until the code behaves.
A simple example:
When Tailwind released v4, models kept mixing v3 and v4 syntax.
No course could fix that.
Only one thing worked:
You learn to prompt your way through it.
That’s the future.
Not memorizing frameworks — but guiding AI through edge cases and inconsistencies.
Learn by iterating, not by studying
Don’t spend hours in tutorials.
Spend 20 minutes getting the concept, then open an LLM and build something immediately.
Tools I recommend:
- Claude (artifacts in browser)
- Lovable (output + UI in browser)
- Claude Code (when you need full control)
- OpenAI’s Codex-style models
Then repeat this cycle:
Prompt → Build → Test → Adjust → Repeat
A personal rule I use:
How close can I get to the final version in one prompt?
That’s how I measure my skill level — not how much syntax I memorized.
Fast builders will win — not perfect planners
A traditional programmer might spend two weeks writing a PRD.
With AI, I can output three versions of an app in one day, test them, and pick the best one.
Is my first version perfect?
No.
Do I get real feedback faster?
Absolutely.
In an AI world, speed to proof beats speed to perfection.
Focus on a problem, not on collecting skills
Old advice says:
“Broaden your skills. Take more courses.”
Here’s the correction:
Broaden your understanding of a problem, not of random skills.
AI rewards:
- people who understand a specific audience, or
- people who understand a specific problem deeply
Not people who hoard knowledge for its own sake.
You’re not here to “learn everything.”
You’re here to apply what AI can do to something real.
What we’re actually gaining — not losing
Some say, “We’re losing the pride of learning deeply.”
I disagree.
What we’re gaining is:
- accessibility
- speed
- feedback loops
- the ability to build without gatekeepers
Deep knowledge still matters — but the definition changed.
Deep knowledge today means:
- Knowing how to think
- Knowing how to prompt
- Knowing how to evaluate AI
- Knowing what to build and why
That’s the real pride:
You make something work in hours, not months.
So how do you prepare for the future of programming?
1. Learn core concepts, not full courses
Just enough to understand what the AI is doing.
2. Practice prompt engineering for debugging
This is the real skill that separates builders from “stuck scrollers.”
3. Build fast, iterate faster
The first version is allowed to be messy.
Shipping teaches you more than any tutorial.
4. Focus on a problem or audience
AI + real-world constraints = powerful combination.
5. Treat AI like your junior dev, not your replacement
Your judgment is still required.
This isn’t the death of programming.
It’s the first time everyone can participate.
