I have a confession to make.

I studied Computer Science.

And if someone had told me years ago that I would eventually be able to describe a software problem to an AI system and watch it produce working code in seconds, I probably would have thought they were describing science fiction.

Yet here we are.

AI can write code.

It can explain code.

It can find bugs.

It can suggest improvements.

It can generate entire functions and, increasingly, help people build applications without understanding every line of code they are using.

So there is an obvious question staring at anyone interested in programming:

Is learning to program still worth it in 2026?

My answer is:

Yes.

But the reason is no longer exactly the same as it was a decade ago.

The old argument for learning programming is changing

For years, the advice was relatively straightforward.

Learn programming.

Learn a language such as C++, Java, Python or JavaScript.

Practice algorithms.

Build projects.

Get a job.

It made sense.

Software was becoming increasingly important, and companies needed people who could write it.

But AI is changing the economics of writing software.

A programmer can now ask an AI system to produce a piece of code that would previously have required considerable time.

That doesn’t make programming irrelevant.

But it does make certain types of programming less valuable.

If your entire value as a programmer is that you can manually type code faster than somebody else, you have a problem.

AI is very good at typing.

Knowing syntax isn’t the same as knowing programming

This distinction is becoming more important.

Suppose I ask an AI to write a Python program.

It produces 200 lines of code.

It looks convincing.

But what happens when:

• the program produces the wrong result?
• the code contains a security vulnerability?
• the architecture doesn’t scale?
• the database design is terrible?
• two components conflict with each other?
• the requirements change?
• the AI misunderstood the actual problem?

Someone still has to figure out what’s wrong.

That someone needs to understand programming.

Not necessarily because they need to personally type every line.

Because they need to understand what the code is supposed to do.

That is a very different skill.

Programming was never really about typing

This is something I wish more people understood.

Programming isn’t fundamentally about memorizing syntax.

It is about problem-solving.

You take a problem.

You break it into smaller problems.

You determine what information you need.

You decide how the pieces should interact.

You design a solution.

You implement it.

You test it.

You discover that your brilliant solution doesn’t work.

You debug it.

Then you discover three more problems.

You fix those too.

The programming language is the tool.

The thinking is the skill.

And AI doesn’t eliminate the need for that thinking.

If anything, it may make it more important.

Imagine two people using the same AI

Person A doesn’t know programming.

They tell an AI:

“Build me an app for managing school attendance.”

The AI produces something.

They copy the code.

They run it.

It works.

Maybe.

Then a user reports a bug.

They go back to the AI.

The AI changes something.

Another thing breaks.

They go back again.

Eventually they are trapped in a cycle of asking the machine to repair something they don’t understand.

Now consider Person B.

Person B understands programming fundamentals.

They know how applications are structured.

They understand databases.

They understand APIs.

They know how authentication works.

They can read code.

They can debug.

They can recognize bad architecture.

They use exactly the same AI.

Who has the advantage?

Person B.

AI didn’t make Person B obsolete.

It made Person B more productive.

And that’s the distinction I think matters most.

The programmer of the future may write less code

This sounds strange, but I think it is increasingly likely.

Imagine a programmer in the future spending less time manually writing boilerplate and more time:

• designing systems
• understanding requirements
• reviewing AI-generated code
• testing applications
• debugging complex problems
• making architectural decisions
• protecting systems
• integrating different services
• understanding users
• deciding what should actually be built

The amount of code they personally type may decrease.

Their responsibility doesn’t necessarily decrease with it.

In fact, it could increase.

This is particularly important for Pakistan

For Pakistani students and graduates, I think this deserves serious attention.

Every year, thousands of young people enter universities believing that a Computer Science degree automatically leads to a good software job.

It doesn’t.

A degree can open a door.

It cannot walk through it for you.

And AI is making the situation more complicated.

The market is unlikely to reward someone simply because they have memorized the syntax of a programming language.

There are millions of people around the world who can generate basic code.

What becomes more valuable is the ability to solve real problems with technology.

That means Pakistani students should be thinking beyond:

“Which programming language should I learn?”

The better question is:

“What can I build with technology that somebody actually needs?”

So what should you learn?

If I were starting Computer Science again today, I wouldn’t begin by trying to learn every programming language.

I’d build a foundation.

1. Learn programming fundamentals

Variables.

Data structures.

Algorithms.

Functions.

Object-oriented programming.

Databases.

Networking.

Operating systems.

Software architecture.

You don’t need to become an expert in every subject.

But you need enough understanding to know what is happening underneath the abstractions.

2. Learn one language properly

Don’t learn ten languages badly.

Pick one.

Python is an obvious choice for many people because of its enormous ecosystem around AI, data and automation.

JavaScript or TypeScript makes sense if you’re interested in web development.

C++ remains valuable in areas where performance and lower-level control matter.

The specific language matters less than developing the ability to think like a programmer.

3. Learn to work with AI

This should now be part of programming education.

Use AI to:

• explain unfamiliar code
• generate prototypes
• write tests
• find bugs
• explore alternative approaches
• document code
• learn unfamiliar libraries
• review your implementation

But don’t blindly accept what it produces.

Read it.

Understand it.

Test it.

Break it.

Fix it.

That’s how you turn AI from a crutch into a tool.

4. Learn software engineering

This may become more important, not less.

Anybody can ask an AI to generate a function.

Building a reliable application is another matter.

Learn about:

• version control
• testing
• databases
• APIs
• authentication
• security
• deployment
• system design
• performance
• maintainability

The difference between a toy and a real product is enormous.

5. Build actual things

This is the part I would emphasize most.

Don’t spend two years watching programming tutorials.

Build.

Build something stupid.

Then build something useful.

Then build something difficult.

Your first application will probably be terrible.

That’s fine.

Your second will probably be slightly less terrible.

Eventually you’ll encounter problems that tutorials never prepared you for.

That’s where real learning begins.

What about people who don’t have a degree?

Here’s some good news.

AI may actually make entering software development more accessible.

You can learn far more cheaply than previous generations could.

You can ask AI to explain concepts.

You can access enormous amounts of documentation.

You can build projects without needing a large team.

You can experiment quickly.

But accessibility cuts both ways.

If everyone can build basic software more easily, basic software becomes less impressive.

That means your projects need to demonstrate something more.

Don’t just build another calculator.

Build something that solves a real problem.

Don’t just make another to-do application.

Find somebody with a genuine problem and solve it.

That’s where technology becomes valuable.

Is “coding” dead?

No.

But I think the definition of coding is changing.

The programmer of 2010 might have spent much of the day turning specifications into lines of code.

The programmer of 2026 may increasingly spend more time turning problems into systems.

That requires programming knowledge.

But it also requires communication, reasoning, product thinking, curiosity and judgment.

The best programmers may therefore become less like human code generators and more like technical problem-solvers.

And AI can become one of the tools they use to do that job.

Don’t learn programming because you want to become a human compiler

Learn it because you want to understand technology.

That distinction matters.

If your only goal is:

“I want someone to pay me for writing code.”

then you should at least recognize that AI is changing that market.

But if your goal is:

“I want to be able to look at a problem and understand how technology could solve it.”

then programming remains incredibly useful.

Because even if AI writes 90 percent of the code, somebody still needs to know what the other 10 percent should accomplish.

And somebody needs to recognize when the 90 percent is wrong.

There is another reason to learn programming

It teaches you how to think.

Programming forces you to be precise.

Computers are extraordinarily unforgiving.

You can’t tell a computer:

“You know what I mean.”

You have to tell it exactly what you mean.

You learn to break complicated problems into manageable pieces.

You learn to debug your assumptions.

You learn that a solution that looks obvious can fail spectacularly when confronted with reality.

Those are useful skills even if you eventually stop writing code professionally.

So, should you learn programming in 2026?

If you genuinely want to learn it:

Yes.

But don’t learn it the way many people learned it ten or fifteen years ago.

Don’t make memorizing syntax your goal.

Don’t assume a degree guarantees a career.

Don’t spend years preparing to write code that an AI can generate in seconds.

Instead:

Learn how computers work.

Learn how software is designed.

Learn how to solve problems.

Learn how to build things.

Learn how to use AI.

And most importantly:

Learn how to decide what is worth building.

That last skill may turn out to be the most valuable one of all.

Because AI can increasingly answer:

“How do I build this?”

But somebody still has to ask:

“What should we build—and why?”

And for the foreseeable future, that’s still a very human question.