In the Age of AI Coding, Is It Still Worth Learning to Program?

From "Idea Is Cheap" to "Code Is Also Cheap" — What Became Expensive?

There's a saying that's been circulating in the programming world for years: "Idea is cheap, show me the code." The point was that everyone has ideas, but only those who can actually ship working code deserve credit. In the past, turning an idea into a running program required real skill. Now, AI coding has dramatically lowered that barrier — as long as you can clearly describe what you want, AI can produce a working demo.

Behind this shift is the breakthrough progress of Large Language Models (LLMs) in code generation. Tools like GPT, Claude Code, GitHub Copilot, and Cursor have been pre-trained on massive open-source code repositories (billions of lines on GitHub, for example), giving them the ability to understand natural language requirements and generate corresponding code. These capabilities are now integrated directly into developers' daily workflows, enabling end-to-end automation from requirement description to runnable code.

The author shared a personal experience: over the past week, he built a PolyMarket quantitative trading bot without writing a single line of code by hand, validating many ideas that had previously been stuck in his notes app. Using GPT at work and Claude Code at home, his AI-assisted coding output has far surpassed what he could produce by typing code manually.

So here's the question: Ideas were already cheap, and now code is cheap too. What's still expensive?

The answer includes at least two things: the courage to start and the ability to judge.

The First Scarcity: The Courage to Start

Many people have plenty of ideas in their heads. They know AI is powerful and could theoretically help them bring those ideas to life. Yet they never take action. Why?

Because for people without programming experience, coding is unfamiliar territory. They instinctively imagine a long list of potential obstacles:

• What do I need to prepare before writing code?
• Isn't setting up the environment incredibly difficult?
• What if I get error messages I can't understand?
• Where exactly do I paste and run the code AI gives me?

These imagined difficulties are almost always far greater than the real ones. Many people get stuck at this stage, blocked by obstacles they've invented in their own minds.

The root cause is this: for many people, the prerequisite for starting something is that their boldness multiplied by their familiarity with the task must exceed the expected psychological threshold. If you know nothing about coding and aren't naturally bold, it's very hard to cross that mental barrier.

From this perspective, having some level of coding ability is still very important — not so you can write every line of code yourself, but to lower the psychological barrier to taking action. The first value of learning to program is that it gives you the courage to take the first step.

The Second Scarcity: Judgment

AI Is Great at Execution, but Not at Judgment

AI is now exceptionally good at execution — tell it what to do and it does it. But it cannot judge whether something is actually worth doing. Whether we call this "product sense" or "business understanding," the fundamental issue is that AI still cannot fully access real-world context: What's your boss's management style? What problem is the requirement actually trying to solve? These judgments can only be made by humans.

Code Review: If You Can't Read It, You Can't Spot the Landmines

The author gave a very concrete example: while recently using AI to write training code, he noticed that for Data Loader implementations, when corner cases arose, AI's go-to solution was often to wrap the exception in a try-except block and move on.

try-except is Python's exception handling mechanism for catching and handling runtime errors. In machine learning Data Loader scenarios, AI tends to use try-except to "swallow" exceptions. While this makes the code appear to run normally, it can mask deeper issues like data corruption or label errors, allowing model training to silently proceed on flawed data and ultimately compromising model quality without any visible warning. This kind of "code runs fine but the results are poisoned" situation is one of the hardest engineering risks to detect.

The code runs, sure — but should you accept this approach? Or should you invest the effort to properly handle those edge cases? Only a human can make that call. If you don't understand code at all, you simply can't tell whether the AI's code is hiding landmines or what trade-offs it's making.

People Who Understand Programming Have Higher Information Entropy When Collaborating with AI

Current AI programming isn't a one-shot process where you say something and get a perfect answer. It requires multiple rounds of dialogue. And the information entropy contained in each prompt written by someone who understands programming versus someone who doesn't is fundamentally different — leading to vastly different outcomes in both quality and efficiency.

Information Entropy is a core concept in information theory, proposed by Claude Shannon in 1948, used to measure uncertainty and information content. In AI programming collaboration, "higher information entropy in a prompt" means the instruction contains more effective, disambiguation-rich technical details — such as specifying data structures, boundary conditions, and performance constraints — enabling AI to understand intent more precisely and generate higher-quality code, dramatically reducing wasted back-and-forth dialogue.

The author used a brilliant analogy:

A person with no medical knowledge goes to the doctor and says: "My stomach really hurts. I had corn porridge last night and couldn't sleep well."

A person with medical knowledge says: "I have tenderness in the right lower abdomen, a low-grade fever for two days, and nausea."

The doctor immediately thinks of appendicitis.

Collaborating with AI on code works the same way — the more you understand programming, the more efficiently you communicate with AI, and the greater the leverage AI provides. This is precisely why, in the age of AI coding, learning to program hasn't become obsolete — it's become more strategically valuable than ever.

The Form of Programming's Value Has Changed

Back to the original question: Is it still valuable for ordinary people to learn programming in 2025?

The answer is yes, but the way programming delivers value has fundamentally changed:

Era	Core Value of Learning to Program
Before	Being able to build a feature or product with your own hands
Now	Being able to improve AI coding efficiency and fully leverage AI's capabilities

In the past, programming ability = productivity itself. Now, programming ability = the lever that harnesses AI productivity.

It's like autonomous driving: no matter how advanced it gets, someone who understands driving principles can still better judge when to take over and when to trust the system. The role of programming knowledge in the AI era has shifted from "driving the car yourself" to "knowing when to hit the brakes."

Final Thoughts

For those looking to get started with programming, Python is currently the most beginner-friendly choice. It's not just that the syntax is clean and the learning curve is gentle — more importantly, Python has become the de facto standard interface language of the AI era: OpenAI, Anthropic, and other leading AI companies offer Python as their primary official SDK, and libraries like NumPy, PyTorch, and LangChain cover the entire pipeline from data processing to LLM integration. The vast majority of AI Agent frameworks are also built around Python. Knowing Python means you can understand how AI is built and how it thinks, rather than just staring blankly at AI's output.

In the age of AI coding, programming is no longer a specialized skill for the few — it's a foundational tool for everyone to amplify their own capabilities. AI hasn't replaced the value of programming; rather, the value of programming has been upgraded from "writing code" to "steering AI to write code." Those who combine domain expertise with programming literacy will gain the greatest leverage in this era.