DEV Community: Shivam Rode

How AI Is Changing Software Engineering in 2026

Shivam Rode — Mon, 29 Jun 2026 13:23:29 +0000

AI isn't replacing software engineers—it's changing what being a software engineer actually means.

Every few weeks, a new headline appears claiming that AI is about to replace programmers. Social media is full of bold predictions: "Nobody will need to code anymore," "Junior developers are finished," or "AI will build entire applications by itself." These statements generate clicks, but they often ignore what software engineering really is.

As someone who's still learning software engineering while building projects and using AI almost every day, I've noticed something interesting. AI has undoubtedly made me faster. I spend less time writing boilerplate code, searching through documentation, or fixing simple syntax errors. Yet despite all these improvements, building good software hasn't suddenly become easy. If anything, the skills that matter most have become even more important.

The biggest change in 2026 isn't that AI can write code—it's that writing code is no longer the hardest part of software engineering.

When I first started programming, I thought software engineers spent most of their day typing code. That assumption didn't last long. The more projects I worked on, the more I realized that a huge portion of development happens before a single line of code is written. You have to understand the problem, design a solution, think about edge cases, decide how different services communicate, and make trade-offs that affect the entire application. AI can generate a React component, a FastAPI endpoint, or even a complete CRUD API within seconds, but it still depends on us to define what the application should do and why one approach is better than another.

Another thing I underestimated was how much time developers spend reading code instead of writing it. Before adding a feature, you need to understand an existing codebase. Before fixing a bug, you have to trace where it originated. Before reviewing a pull request, you need to understand another developer's thought process. Professional software engineering is rarely about creating something from scratch; it's about improving and maintaining systems that already exist.

This is where AI has genuinely changed my workflow. Instead of spending half an hour trying to understand a complicated function, I can ask AI to explain it. If I'm learning a new framework, I can ask questions in plain English instead of reading documentation for hours. AI doesn't replace understanding, but it dramatically reduces the time needed to build that understanding. Used correctly, it feels less like an automatic code generator and more like having an experienced developer available whenever you're stuck.

A lot of people also believed that "prompt engineering" would become the most valuable skill. After using AI for months, I don't think that's true. The quality of AI-generated code depends far more on the quality of the problem you're trying to solve than on some magical prompt. If your requirements are unclear, the output will be unclear. If your architecture is poorly designed, AI will happily generate code that follows that poor design. Good prompts can improve results, but they can't replace clear thinking. In many ways, AI rewards developers who already understand software engineering fundamentals.

One concern I hear often is that developers will become too dependent on AI. While that risk certainly exists, I've also seen the opposite happen. AI can be an incredible learning tool. It explains unfamiliar concepts, helps debug errors, suggests improvements, and introduces tools or libraries I might never have discovered otherwise. Instead of spending hours searching forums, I can ask a question, understand the answer, verify it, and continue building. The important part is verification. Copying AI-generated code without understanding it is no different from copying code from Stack Overflow without reading it. The developers who gain the most from AI are the ones who question its answers, test its suggestions, and learn from the explanations.

Ironically, AI has also made debugging even more valuable. AI writes bugs too—sometimes very convincing ones. It can produce code that looks clean, follows best practices, and still fails in production because of an overlooked edge case or incorrect assumption. That's why engineers who understand debugging, networking, databases, performance optimization, and system design remain incredibly valuable. Writing code is becoming easier; understanding why something doesn't work is still one of the hardest parts of software engineering.

Perhaps the biggest impact of AI is that it gives small teams enormous leverage. Tasks that once required several developers can now be completed by one or two people using AI effectively. Boilerplate code, documentation, test generation, and routine refactoring can all be accelerated significantly. That doesn't mean software engineers are becoming less important. It means they can spend more time solving meaningful problems instead of repetitive ones.

If someone asked me what they should learn to become a software engineer in 2026, I wouldn't tell them to memorize every framework or language feature. AI is already good at syntax. Instead, I'd tell them to focus on fundamentals: system design, databases, networking, security, debugging, testing, clean architecture, and communication. These are the skills that don't become obsolete when new tools arrive. In fact, the stronger your fundamentals are, the more effectively you can use AI.

History has shown that every major technological advancement creates fear. People believed integrated development environments would replace programmers. Then they thought Stack Overflow would make developers lazy. When AI coding assistants arrived, many predicted the end of software engineering altogether. Instead, each new tool simply changed how developers worked and made productive engineers even more productive.

I believe AI is following the same pattern.

Software engineering has never been defined by how quickly someone can type code. It's about understanding problems, making thoughtful decisions, designing reliable systems, and creating products that people actually want to use. AI can generate thousands of lines of code in seconds, but it still relies on humans to provide direction, make trade-offs, and take responsibility for the final product.

I don't think software engineering is disappearing. I think it's evolving. The developers who embrace AI while continuing to strengthen their fundamentals will have an incredible advantage over the next decade. AI isn't replacing software engineers—it is simply changing the way we build software.

And maybe that's exactly what every great technological shift has done.

What do you think? Has AI changed the way you build software? I'd love to hear your perspective in the comments.

Competitive Programming

Shivam Rode — Sun, 28 Jun 2026 08:01:54 +0000

Shivam Rode

Jun 28

Competitive Programming explained for beginners.

#competativeprogramming #dsa #coding #beginners

2 min read

Competitive Programming explained for beginners.

Shivam Rode — Sun, 28 Jun 2026 08:01:01 +0000

Competitive Programming Explained for Beginners

When I wanted to start competitive programming, I spent a lot of time exploring what it actually was. As a beginner, I was very confused. One question that constantly came to my mind was, "Aren't DSA and Competitive Programming the same thing?" It took me some time to understand the difference between the two.

If you're a beginner who wants to start your competitive programming journey and has similar doubts, then this article is for you.

What is DSA?

DSA stands for Data Structures and Algorithms. It is the study of different data structures and algorithms and how they can be used to solve problems efficiently. The main objective is to find the most optimal solution with respect to time complexity and space complexity.

When practicing DSA, the focus is on understanding the problem, choosing the appropriate data structure, selecting a suitable algorithm, and implementing the solution yourself. There is no competition involved—it's simply about improving your problem-solving skills.

What is Competitive Programming?

Competitive Programming (CP) is a coding sport where programmers compete against one another by solving algorithmic problems within a fixed time limit.

Each contest consists of multiple problems, and every solution is tested against a set of hidden test cases. To get a problem accepted, your program must produce the correct output for all test cases while staying within the given time and memory limits.

Unlike regular DSA practice, competitive programming is not always about writing the most optimized solution possible. If a simpler approach successfully passes all the test cases within the given constraints and takes less time to implement during the contest, it is often the better choice.

After the contest, participants are ranked based on the number of problems solved, the time taken, and any penalties for incorrect submissions.

How Ratings Work

Most competitive programming platforms have a rating system. Your rating changes after every rated contest.

If you perform better than expected and outperform participants with similar ratings, your rating increases. If your performance is below expectations, your rating decreases. As your rating grows, you'll face stronger competitors and more challenging problems.

Popular Competitive Programming Platforms

Some of the most popular platforms for competitive programming are:

Codeforces
CodeChef
AtCoder
LeetCode

Each platform has its own style of contests and difficulty levels, making them suitable for learners at different stages.

Final Thoughts

Competitive Programming and DSA are closely related, but they are not the same. DSA helps you build the knowledge needed to solve problems efficiently, while Competitive Programming provides a platform to apply that knowledge under time pressure while competing with others.

If you're just starting out, don't worry if the problems seem difficult at first. Every experienced competitive programmer was once a beginner. Practice consistently, learn from your mistakes, and enjoy the process.

Thanks for reading!