In a world where artificial intelligence seems to write its own code, draw its own art, and even design its own algorithms, many people are asking a simple but powerful question: Do we still need to learn coding? – Andrew Ng.
It’s a fair question. With tools like ChatGPT, Gemini, and GitHub Copilot capable of generating code from plain English, one might wonder if traditional programming is becoming obsolete.
But Andrew Ng — one of the most respected voices in AI, co-founder of Google Brain, and founder of DeepLearning.AI — has a very different answer.
He believes that coding is not dying — it’s evolving.
And perhaps, it’s more important than ever before.
Let’s unpack what he means, why it matters, and where we stand in this debate.
🧠 The Case Andrew Ng Makes: Coding Is Still a Core Skill
Andrew Ng argues that coding remains an essential part of understanding how machines think. He doesn’t see it as a technical burden — but as a language, a literacy.
When the printing press was invented, literacy didn’t become less important — it became universal. Similarly, in the age of AI, understanding the “language of logic” is the new literacy.
Ng’s key point is this: AI can help you write code faster, but you still need to understand what it’s doing.
If you don’t know the structure of a program, or what good code looks like, how can you evaluate or improve what AI gives you?
Coding isn’t just about typing commands — it’s about thinking logically, breaking down problems, and creating systems. These are mental habits, not mechanical ones.
💡 Coding as a Superpower in the AI Era
Think about it: the tools that are replacing “manual coding” are themselves built by people who understand how code works.
Even prompt engineering — the skill of communicating with AI — borrows the mindset of programming:
- Inputs and outputs.
- Logic and structure.
- Testing and refinement.
Andrew Ng says that AI has lowered the barrier to programming, not removed it. What used to take years of learning syntax can now be achieved with guided natural-language instructions.
But here’s the catch: only those who understand the logic behind the curtain can truly harness the power of AI tools.
In his view, everyone — from artists to entrepreneurs — should learn the fundamentals of coding, not to become engineers, but to become creators in a digital world.
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🔍 Why Coding Literacy Still Matters
- AI Isn’t Perfect — It Predicts, Not Thinks
Generative AI doesn’t understand context like a human. It predicts what comes next based on patterns in data.
When it generates code, it’s still making probabilistic guesses — not decisions. If you don’t understand how code works, you won’t know whether the AI output is efficient, secure, or even correct. - Automation Requires Oversight
In every automation cycle, humans don’t disappear — they move higher up the ladder.
Knowing how to read and tweak code gives you control over what’s being automated. - Coding Teaches Problem Solving
Coding forces you to think systematically. It teaches you to deconstruct problems into smaller parts, a skill that extends far beyond software. - Coding Bridges Human and Machine Thinking
Every tool we use — from AI chatbots to mobile apps — is a translation between human goals and machine logic.
The better you understand that logic, the better you can communicate with AI.
⚙️ The Changing Nature of Coding
However, it’s important to recognize that what we call “coding” today doesn’t look like it did even five years ago.
We’re moving from syntax coding to conceptual coding.
Instead of memorizing every function, the modern programmer focuses on expressing intentions clearly and verifying outcomes intelligently.
In other words — you don’t need to know every command.
You need to know what to ask, how to validate, and where to adjust.
AI tools like Copilot or Replit Ghostwriter can now autocomplete entire blocks of code. But someone still needs to review that logic, ensure the flow is efficient, and verify it meets business goals.
That “someone” is you — and that’s why coding isn’t obsolete. It’s simply transformed into a more strategic skill.
🚀 Why We Agree with Andrew Ng
We agree with Andrew Ng for a few powerful reasons.
1. Coding Is the Grammar of AI
If AI is the new language of innovation, coding is its grammar.
Without grammar, you might speak, but you won’t articulate.
Even as tools abstract away technical details, the underlying logic remains the same. Understanding code is like understanding how your car’s engine works — you don’t have to rebuild it, but you should know when something feels off.
2. Learning to Code Builds Cognitive Muscle
Coding disciplines the mind.
It rewards persistence, curiosity, and logical reasoning.
You debug. You fail. You learn.
Those mental loops mirror the very process AI uses to learn — iteration, feedback, correction.
In a sense, learning to code makes you think like an algorithmic mind — but stay human enough to direct it.
3. Code Is the Foundation of Every Future Skill
Whether it’s prompt engineering, AI workflow automation, or data-driven marketing — all these roles require an understanding of how logic flows through systems.
You can’t truly master automation without understanding what you’re automating.
Learning code gives you the blueprint for every digital process, no matter how advanced the tool becomes.
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🤖 The Counterpoint: “AI Writes Code — Why Should I?”
It’s fair to ask — if AI can generate code better and faster than humans, why should we bother?
Let’s unpack that argument.
AI-generated code is excellent at pattern replication but not at innovation. It can optimize, but not originate. It can imitate solutions, but not design them from first principles.
Just like calculators didn’t kill math, AI won’t kill coding.
When calculators arrived, people said mental math was over. But understanding math became more important — because humans moved from calculation to application.
Similarly, as AI takes over repetitive coding, humans move toward conceptual design — asking the right questions, guiding the system, and architecting outcomes.
Without coding knowledge, you become dependent on AI’s decisions — even when they’re wrong. With coding knowledge, you stay in control.
⚡ The Hybrid Future: “Vibe-Coding” and No-Code Platforms
There’s no denying the rise of “no-code” tools and “vibe-coding” (using natural language to build programs).
These tools empower millions of non-technical people to create apps, workflows, and automations effortlessly. That’s progress — not competition.
But here’s what most people miss: the creators of no-code platforms are coders themselves.
No-code tools remove friction for users, not fundamentals for developers.
In fact, the better you understand coding logic, the more powerful your no-code creations become.
You can push limits, debug errors, and build hybrid workflows that combine the best of both worlds.
🧩 Why Non-Coders Should Still Learn the Logic of Code
You don’t need to become a software engineer.
But you should learn computational thinking — the ability to think in structured steps.
For example:
- When you automate an email sequence, you’re defining if-then logic.
- When you design a workflow in Notion or Zapier, you’re building functions and triggers.
- When you prompt AI, you’re essentially writing declarative instructions.
See the pattern? That’s coding — disguised in plain language.
Andrew Ng’s message isn’t “learn Python or Java.”
It’s “learn to think like a coder.”
That skill translates to every domain: marketing, design, operations, education, even content creation.
💬 The Human Advantage
Here’s the irony — AI might someday write perfect code, but it will still need humans to tell it why something should be built.
Humans bring context, ethics, emotion, and purpose — things machines don’t have.
Coding helps us express that purpose in a form machines can understand.
It’s not just about software — it’s about translating human intention into digital reality.
As Ng often says, “AI is the new electricity.”
And what good is electricity if you don’t know how to wire it safely?
🧭 Our Stand: Learning Coding Is Learning Relevance
Let’s imagine two futures.
Future A: You rely entirely on AI. You type a request, get a result, and accept it without question.
Future B: You collaborate with AI. You understand its logic, improve its output, and innovate faster.
Which future do you want?
The second one is only possible when you understand how the system works — and that begins with coding literacy.
Learning to code doesn’t just teach you a technical skill; it rewires how you think about problems.
It builds confidence in a world of uncertainty.
It turns you from a consumer of AI into a creator of possibilities.
🌍 The Bottom Line
Andrew Ng’s message is not nostalgic. It’s visionary.
He isn’t defending old-school coding; he’s redefining what it means.
Coding today is less about syntax and more about structure, less about typing and more about thinking.
Even if AI handles 90% of the mechanics, the 10% that remains — human understanding — is where real power lies.
So, should you still learn to code?
Absolutely.
But learn it differently.
Learn it as a language of systems, not just software.
Learn it as a bridge between imagination and execution.
Learn it to understand the mind of machines — so you can guide them, not be guided by them.
Final Reflection
Andrew Ng’s perspective isn’t just about programming — it’s about preserving human agency in a machine-driven world.
AI can predict, generate, and automate.
But only humans can decide what matters, what’s ethical, and what’s worth building.
Coding is how we speak that decision into existence.
It’s not just a technical act — it’s a creative declaration:
“I understand how this world is built,
and I choose to build within it — consciously.”
That’s why, even in 2025 and beyond, learning to code remains one of the smartest decisions you can make.