How to Be a Good Programmer in the Age of AI

January 08, 2026

Introduction

AI models are remarkably capable and can accomplish tasks faster than many new and intermediate developers. Given this, you might wonder why bother upskilling yourself when you can simply prompt your way to solutions with minimal effort. The answer is: it matters more than ever.

Now is the time to leverage AI strategically to accelerate your learning and growth.

Don’t Skip the Fundamentals

The most important rule: don’t dismiss the advice of experienced programmers about AI usage. They’ve written hundreds of thousands of lines of code and built entire applications from scratch, which enables them to evaluate AI-generated code critically and distinguish quality from mediocrity. This skill comes from years of dedicated work.

Yes, you can use the same AI model, but the output quality won’t match what experienced engineers achieve. It’s not just about generating code—it’s about maintaining it. What happens when you generate code for a large application and later lose access to the AI model? Can you understand that code well enough to modify or maintain it on your own?

Reading and understanding code has never been more critical. If you don’t fully comprehend what you’re working with, invest the time to understand it thoroughly.

Computer science fundamentals don’t change much. Data structures, design patterns, and algorithms remain core to programming. With solid fundamentals, you can identify when AI suggests a suboptimal data structure or flawed algorithm, and guide it toward the correct solution. To someone lacking these foundations, a flawed AI solution might appear correct.

AI won’t replace these fundamentals. In fact, those who excel at writing and improving AI-generated code are invariably strong in these areas.

A Practical Workflow for AI-Assisted Development

Plan first. Read the specifications and sketch out the structure of your solution. Write this plan down, then consult AI to think through it with you and catch anything you missed.
Write code by hand. Use an editor or IDE without AI integration, or disable it temporarily. This keeps you sharp and ensures you retain the ability to write code independently.
Generate tests early. Once your code is ready, ask AI to generate test cases based on your plan. Run them. Your hand-written code likely has edge cases you missed, and tests will catch them. Fix the failing tests yourself and look for similar issues. In the AI era, when test-writing costs have plummeted, skipping unit tests is inexcusable.
Get an AI code review. When your code works and matches your plan, ask AI to act as a senior engineer and review it for correctness, maintainability, best practices, and quality. Export this review to a file, open it alongside your code, and work through the feedback methodically. Keep running tests as you make changes.
Write good commit messages. Use AI to generate clear, descriptive commit messages and pull request descriptions. This makes your contributions easier for reviewers to evaluate and appreciate.

Read Good Code and Build Judgment

Invest time in reading well-written code to develop an eye for quality. AI can generate excellent code, but understanding it is your responsibility. If all you did was write prompts and ended up with code you don’t fully understand, you’ve essentially hired a consultant and claimed credit for their work. Short-term wins don’t matter if you’ve created a codebase you can’t maintain.

Know AI’s Strengths and Limits

AI excels as a research assistant and prototyping partner. It’s remarkably good at generating personal projects and scripts where rigor isn’t critical. Production code is a different story. The stakes are higher, and quality matters.

Be Honest About Attribution

Integrity is more important now than ever. Declare the extent of AI-generated code in your work. Don’t claim ownership until you’ve developed a deep understanding of it and could maintain it without AI assistance. Anything less is technical debt, not value.

The Competitive Advantage

AI is a powerful tool in the hands of experienced engineers who maintain strong fundamentals and keep their coding skills sharp. For those who overuse AI without understanding it or prioritize short-term productivity over learning, the outlook is grim. They risk becoming easily replaceable.

Conclusion

This is not the time to relax or cut corners. Work hard to develop solid skills and keep yourself sharp. The programming landscape is transforming rapidly, and no one knows what the field will look like even a year from now. The question isn’t whether to use AI—it’s whether you’ll remain a skilled engineer capable of evaluating and improving what AI produces.

Vinit Kumar