One of the biggest questions people are asking in 2026 is whether learning to code is still worth it. It’s a question that’s on the minds of many people, especially younger students who are considering studying Computer Science or pursuing a career in software development. With the rapid rise of artificial intelligence, it’s understandable why so many people are questioning whether programming is still a valuable skill to invest in.
If you asked me this question, my answer would be a simple and confident yes. Despite all the excitement surrounding AI, learning to code remains one of the most valuable skills you can develop. The reality is that AI is changing the way software is built, but it is not replacing software engineering itself.
AI IS AUTOMATING TASKS, NOT SOFTWARE ENGINEERING
Much of the concern about AI comes from the belief that it will replace software engineers altogether. While AI has undoubtedly become an incredibly powerful tool, it’s important to understand what it actually excels at.
AI is very good at automating repetitive, low-judgement work. It can generate code, explain programming concepts, suggest improvements, and speed up many parts of the development process. However, software engineering involves much more than simply writing lines of code.
Software engineers are still responsible for deciding what should be built in the first place. They design the overall architecture of software systems, make technical decisions, review and validate code, debug production issues, and ultimately take responsibility for the quality and reliability of the software they deliver.
Even if AI generates a large portion of the code, someone still needs to determine whether that code is correct, secure, maintainable, and suitable for the problem being solved. No professional engineering team simply copies and pastes AI-generated code directly into production without carefully reviewing it.
This is why understanding programming fundamentals is becoming even more important in the AI era. You cannot effectively evaluate AI-generated code if you don’t understand programming yourself. If you don’t know how something works, you won’t know when it’s wrong, and you certainly won’t know how to fix it when problems arise.
IF YOU’RE STARTING TODAY, LEARN PYTHON
Another question I get asked regularly is which programming language beginners should learn first. My recommendation is always Python.
That doesn’t mean Python will be the only language you’ll ever need to learn. As your career develops, you’ll almost certainly pick up other languages depending on your area of specialisation. However, if you’re starting from scratch in 2026, Python is the language I would recommend above all others.
One of the biggest reasons is that Python is incredibly beginner-friendly. When I first learned to code many years ago, I struggled with languages like Java because there was so much syntax to understand before you could even focus on programming concepts. Python, on the other hand, has a much cleaner and simpler syntax, allowing beginners to concentrate on learning how to think like a programmer instead of getting lost in complicated language rules.
Python has also become the dominant programming language in artificial intelligence and machine learning. Many of today’s most popular AI frameworks, including PyTorch, TensorFlow, Hugging Face, and LangChain, are all built around Python. If you’re interested in working with AI, automation, machine learning, or data science, Python provides an excellent foundation.
On top of that, Python remains one of the most in-demand programming languages in today’s job market. While nobody can accurately predict what the technology landscape will look like five or ten years from now, Python continues to offer excellent career opportunities across multiple industries.
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MASTER THE FUNDAMENTALS BEFORE ANYTHING ELSE
One mistake many beginners make is rushing straight into AI frameworks, web development, or complex libraries without first building a solid understanding of programming fundamentals.
Your first priority should be learning the core concepts of Python. This includes understanding variables, data types, conditional statements, loops, functions, lists, and dictionaries. These concepts form the foundation of everything you’ll build later in your programming journey.
If there’s one topic I’d encourage learners to spend extra time mastering, it’s dictionaries. Understanding key-value pairs, nested dictionaries, dictionary methods, and how to iterate through dictionaries will serve you extremely well because these structures appear constantly in real-world Python applications. Whether you’re working with APIs, configuration files, or JSON data, dictionaries are everywhere.
A strong foundation makes every advanced concept easier to understand later.
LEARN HOW REAL SOFTWARE IS BUILT
Once you’re comfortable with the fundamentals, the next step is understanding how professional software is structured. This is where object-oriented programming becomes important. Learning about classes, objects, methods, and how software components interact with one another helps you understand how larger applications are designed and maintained. Most modern Python libraries and frameworks make extensive use of object-oriented programming, so becoming familiar with these concepts is an essential step in your development as a software engineer.
Equally important is learning how to handle errors effectively. Every developer encounters bugs, syntax errors, runtime errors, and unexpected behaviour. Becoming comfortable with techniques such as exception handling, reading stack traces, and debugging code is one of the most valuable skills you can develop.
In reality, software engineering isn’t just about writing code. A significant amount of time is spent identifying problems, testing solutions, and fixing bugs before software is ever released to users.
BUILD PROJECTS THAT DEMONSTRATE REAL SKILLS
After you’ve built a solid understanding of Python, it’s time to apply your knowledge by building projects. One piece of advice I always give beginners is to avoid creating the same portfolio projects that everyone else builds. Projects like to-do lists, simple calculators, and basic note-taking applications are perfectly acceptable for learning, but they don’t do much to distinguish you from thousands of other aspiring developers.
Instead, challenge yourself to build projects that interact with real-world systems. Create applications that consume public APIs, build Discord bots, develop automation scripts, write web scrapers, or create tools that solve practical problems. Projects like these demonstrate that you can work with external systems, understand real software development practices, and build applications that go beyond the basics.
A strong portfolio isn’t about having the largest number of projects; it’s about demonstrating meaningful technical skills through thoughtful and practical work.
USE AI AS A LEARNING TOOL, NOT A CRUTCH
Of course, no discussion about programming in 2026 would be complete without talking about AI itself. Yes, AI can write code. That much is undeniable. The real question isn’t whether AI can generate code, it’s whether you’re using AI in a way that actually helps you become a better software engineer.
One of the biggest mistakes I see is people relying on AI before they’ve even attempted to solve a problem themselves. They’ll copy and paste AI-generated code into their projects without understanding what it does, hoping everything will simply work.
In my opinion, that’s one of the worst ways to use AI because it prevents you from developing the problem-solving skills that every good engineer needs.
Instead, treat AI as a mentor rather than a replacement for your own thinking. Ask it to explain unfamiliar code, review solutions you’ve already written, suggest improvements, or teach you concepts you don’t fully understand. This allows you to learn while still benefiting from AI’s capabilities.
The best approach is to solve the problem independently first. Once you’ve reached a solution, ask AI for feedback, alternative approaches, or possible optimisations. That way, you’re strengthening your own reasoning while using AI to accelerate your learning instead of replacing it.
THE OPPORTUNITIES ARE STILL TTHERE
Despite all the discussion around AI, Python continues to open doors to a wide range of career opportunities. It provides a pathway into backend software engineering, data engineering, data science, machine learning engineering, and AI engineering, among many other roles.
The demand for people who understand software fundamentals has not disappeared. If anything, it has become even more valuable because organisations need engineers who can work effectively with AI rather than simply depend on it.
FINAL THOUGHTS
So, let’s return to the question we started with: Is learning to code still worth it in 2026? Without hesitation, my answer is yes.
AI is transforming software development, but it is not replacing software engineers. Instead, it is changing the tools we use and increasing the importance of strong programming fundamentals. The engineers who will thrive over the coming years won’t be the ones who blindly rely on AI to write their code. They’ll be the ones who understand how software works, can solve problems independently, and know how to use AI as a tool to enhance their productivity.
Learning to code is still one of the best investments you can make. Start with the fundamentals, build meaningful projects, learn how software systems work, and use AI wisely. If you do that, you’ll be well positioned to succeed, not just in 2026, but for many years to come.
Hi, I’m Dr. Mobolanle Bello, a Computer Science Lecturer with over 15 years of experience in software engineering, web development, data science, database management, ebusiness, research, and education. My expertise includes creating bespoke digital solutions such as e-learning platforms and e-commerce sites, alongside conducting research in AI, machine learning, and network optimisation. I have published my work in respected journals and conferences, contributing to advancements in technology. Outside of academia, I am passionate about using technology to improve lives and am committed to inspiring the next generation of tech professionals through teaching and collaboration.
