Should You Still Learn to Code in the Age of AI?

Has AI Made “Learn to Code” Obsolete?

It’s 2027. You just built a complex website for a client — alone and in one week. A decade ago, that same project would have taken a team months to complete. Today, AI coding tools handle much of the heavy lifting, letting individuals deliver results faster than ever.

Learning to Code: Then and Now

For years, “learn to code” was solid advice for career changers and job seekers. Software engineering offered high starting salaries, and coding seemed accessible through online bootcamps. Yet, coding requires a kind of logical thinking not everyone finds natural.

Now, AI has made coding far more accessible. Instead of writing every line, you describe what you want, and AI generates the working software. The U.S. Bureau of Labor Statistics projects a 10% decline in computer programmer jobs between 2023 and 2033, with rising unemployment among recent graduates. So, is learning to code still worth it?

The Evolution of Programming

Programming began as a military tool. The ENIAC, built to calculate artillery firing tables, required mathematicians to manually configure switches and punch cards — a slow, complex process. In 1948, ENIAC was upgraded to store programs internally, speeding things up.

By the late 1950s, higher-level programming languages let users type commands instead of using punch cards. The 1970s introduced video monitors, enabling direct code editing on screen. The 1980s brought personal computers, democratizing access to programming. New tools like integrated development environments (IDEs) combined editing and debugging, improving efficiency.

The internet expanded opportunities for coders, fueling demand for websites, games, and apps. Programming languages diversified, with some aimed at beginners. By the 2010s, autocomplete features used context to suggest code, hinting at the AI tools to come.

The AI Era of Coding

Today’s AI coding assistants are trained on massive code databases, capable of writing entire blocks of code from simple prompts. Microsoft’s GitHub Copilot, built on OpenAI’s Codex, was an early breakthrough. It started as a tool for generating small snippets and has evolved into a system that can handle broader goals autonomously.

Copilot’s “agent mode” allows it to break down complex tasks, write code, test it, and fix mistakes without constant user input. This has boosted AI-generated code from about 25% to over 40% of developers’ output, with some scenarios fully handled by AI.

Major tech companies report large portions of new code are AI-generated. Google estimates 25% as of 2024, Microsoft 20-30% by early 2025, and predictions suggest this will rise to 90% or more within a few years.

Is “Learn to Code” Still Good Advice?

Yes — but the meaning of coding is shifting. What once required deep knowledge of syntax and logic now often means knowing how to communicate effectively with AI tools. The skill of “prompting” AI to generate useful code will become essential.

GitHub CEO Thomas Dohmke compares this shift to past advances: from configuring hardware to typing code, now to speaking or typing ideas for AI to execute. He envisions millions using these tools to create personalized apps, solving problems companies won’t address.

This means teaching prompt skills early, helping people express their ideas clearly to AI. Like how smartphones became natural for Gen Z, AI agents will become everyday collaborators.

The Role of Professional Developers

Professional software engineers remain crucial. AI handles routine or well-defined coding tasks, freeing developers to focus on complex problems. The nature of programming work is evolving, requiring deeper expertise in architecture, system design, and integration.

Past transitions gave programmers years to adapt, but AI’s impact is faster and broader, automating much of the software development pipeline — from code generation to deployment and maintenance.

As entry-level coding roles diminish, the bar for developers will rise. However, those who adapt will tackle bigger challenges and deliver more value. AI won’t replace developers; it will change what they do.

Looking Ahead

• AI expands who can create software by simplifying coding tasks.
• Learning to code will increasingly mean learning to prompt AI effectively.
• Professional developers will shift focus to complex, high-level challenges.
• Early education should include AI collaboration skills to prepare future generations.

For those interested in expanding their AI and coding skills, resources like Complete AI Training offer courses on AI tools, prompt engineering, and automation to stay ahead in this evolving landscape.