If you’re wondering whether learning to code in 2026 is still worth it, I get it, I was sitting in the exact same spot not too long ago, asking the exact same question.
When I was learning to code, I almost stopped. Not because it was too hard. Not because I wasn’t making progress. But because I opened YouTube one day and fell into a rabbit hole of videos telling me that coding was dead, that AI had replaced developers, that every line of HTML and Python I was writing was essentially practice for a job that no longer existed.
I remember staring at a half-built project on my screen, genuinely asking myself: what’s the point?
That moment almost cost me my career. And if you’re reading this right now, feeling the same thing, uncertain, overwhelmed, wondering whether you’ve already missed the window, then this article is for you. Not for a generic beginner. For you, specifically, in this exact moment of doubt.
My name is Khawaja Abdul Mujeeb and I work as an Associate Software Engineer, where I lead full-stack engineering, conduct code and architecture reviews, and ship production-grade software that real companies depend on.
I’ve built intelligent RAG pipelines, fine-tuned large language models, architected real-time financial systems, and deployed full-stack applications to AWS Lambda. My Final Year Project was literally a video version of Shazam, identifying movies from short clips using CLIP vision transformers and FAISS-based similarity search.
None of this came from AI doing my work for me.
I want to walk you through how I actually learned to code, what I got wrong, what I eventually got right, and, most importantly, what I think you should do if you’re starting today. Because the picture of coding in 2026 looks completely different depending on who you’re listening to, and most of the loudest voices are wrong.
Start Here: Define the Destination Before You Pack the Bag
The single biggest mistake I see beginners make, and a mistake I made myself, is starting to learn without knowing what they’re learning toward. They pick up a Python tutorial on Monday, switch to a JavaScript crash course on Wednesday, and by Friday they’re three hours deep into a React video wondering why nothing is connecting.
I’ve been there. It’s an exhausting loop and it teaches you almost nothing.
Before you write a single line of code, answer this question honestly: what do you actually want to build? A web application? An AI product? A mobile app? A backend system? A data pipeline? Your answer isn’t permanent, mine shifted multiple times, but it gives you a starting point that’s actually yours, not just the topic that happened to trend on social media that week.
If you have no clear direction yet, and that’s completely fine, here’s what I’d tell you: start with Python.
The numbers speak for themselves. According to the latest TIOBE Index, Python has cemented itself as the world’s number one programming language, commanding an unprecedented 21% of the total global market share.
Stack Overflow’s most recent data shows that 57.9% of developers worldwide now actively use Python, which recently allowed it to officially overtake JavaScript as the most active language on GitHub for the first time in a decade.
The U.S. Bureau of Labor Statistics projects a 34% growth in data science and AI roles over the next ten years, nearly all of which run on Python.
What does that mean for your salary? Entry-level Python developers earn between $70,000 and $96,000 per year. Mid-level developers climb to $100,000–$130,000. Senior engineers and architects clear $140,000 to over $200,000 annually.
Specialize in data science and you’re looking at an average of $150,000. Step into AI and ML engineering and the top-tier range sits between $120,000 and $165,000 per year.
I personally built the backend core of VidEx, the video processing pipeline, the CLIP embedding generation, the FAISS similarity search, all in Python. Without a genuine, hands-on understanding of the language, none of that would have been possible.
The Learning Path That Actually Worked for Me
I want to be specific here because vague advice is useless. Here’s the actual path I followed, refined based on what got me hired, promoted, and trusted with increasingly complex systems.
Learn The Fundamentals.
There is a massive difference between knowing Python’s syntax and understanding how Python thinks. The syntax tells you what to write. The fundamentals tell you why it works. When I started, I learned data types, control flow, functions, and file handling, but more than that, I learned why a list is mutable but a tuple isn’t. I learned what happens in memory when you pass a function to a dictionary. That depth is what separates someone who can follow a tutorial from someone who can solve a problem they’ve never seen before.
Get a Formal Certificate Early.
I earned my Introduction to Python certificate through 365 Data Science, and I know it sounds old-fashioned in an era of self-teaching. But here’s what the certificate actually did for me: it forced me to complete a structured curriculum instead of jumping around.
It gave me a tangible milestone. And it gave me something concrete to show in interviews when I had no professional experience yet. Later, I added IBM AI Fundamentals, Microsoft’s Advanced AI and Machine Learning certification, and Cisco’s CyberOps Associate.
Certifications add structure, making it easier to keep progressing, and it also creates a positive impact on your professional profile.
Build Something Small. Then Break It. Then Fix It.
I started with a calculator. It sounds embarrassing to say now, but that calculator taught me more about Python logic flow than the first two weeks of tutorials combined.
That’s when I realized that tutorials can only take you so far. Real learning begins when you start building on your own. It’s frustrating at first, you’ll get stuck, make mistakes, and spend hours debugging problems that seem impossible.
But those challenges teach lessons that no tutorial ever can because you’re forced to think, experiment, and solve problems independently.
These helped me move from small Python scripts to advanced web crawlers. I built an automated e-commerce intelligence system that tracked competitive pricing signals across 1,000+ product SKUs for a UK marketplace in near real-time, using Selenium, BeautifulSoup, and Pandas.
Solve Problems Every Single Day.
When I was building my fundamentals, I solved five to ten problems daily on Cody. You can also use HackerRank or LeetCode, whichever clicks for you. But if you want everything in one place, I’d point you toward the Scenario-Based Problems by Syntax Scenarios.
Unlike standard coding exercises, these put your knowledge into real-world situations. That’s a completely different and much more honest test of whether you actually understand something, because the problem doesn’t tell you which concept to use. You have to figure that out yourself.
Either way, the point is developing pattern recognition. Problems force you to confront the difference between what you think you understand and what you actually understand, and that distinction gets discovered at midnight when a test case fails for a reason you can’t immediately see. That discomfort is the education.
Dry Run Your Code
Before you run your code, read through it line by line and predict what it will do. This habit, called dry running, is one of the most underrated ways to sharpen your logic.
When I was building my fundamentals, I used to get confused about why a variable held a different value than I expected, or why a loop ran one extra time. Tracing each value manually, step by step, forced me to actually understand what I was writing instead of just hoping it worked.
If pen and paper feels difficult at first, Syntax Scenarios has a Code Visualizer built for exactly this. You watch your code execute line by line, see how variables change after each step, and immediately spot where your logic breaks. It’s the closest thing to having the code explain itself to you.
This one habit separates beginners who copy-paste until something works from developers who understand why it works.
Understand How Systems Connect
This is the step most beginners skip entirely, and it’s also the one that matters most at the professional level. How does a frontend actually communicate with a backend? How does authentication logic work? What happens when a database query runs slowly and brings down an API?
This systems-level thinking is what takes a developer from “someone who writes code” to “someone who ships products.” And that is what matters most nowadays, because anyone can build a product but not everyone can engineer it in the best possible way.
I developed it by being part of building fintech products, understanding how a financial engine needs to behave under real transactional pressure.
Sit With Bug Until You Understand It.
One thing I want to specifically say about HTML and CSS, because I think it’s where a lot of beginners give up too quickly: push yourself until you can look at any design and immediately see its structure in your head.
Until you can look at a layout and know exactly which Flexbox properties produce it. That fluency doesn’t come from watching tutorials.
It comes from spending hours, sometimes frustrating, sometimes infuriating hours, on a single bug that won’t resolve, refusing to copy-paste a solution until you understand why the solution works.
I remember spending an entire afternoon on a single animation responsiveness issue. When I finally fixed it, I understood CSS positioning at a depth I couldn’t have reached any other way. That afternoon of frustration was worth ten tutorials.
The World You’re Entering Has Changed, But Not the Way You’ve Been Told
Now I want to talk about AI. Not to scare you and not to hype it. But to give you the clearest, most honest picture I can, the one I wish someone had given me.
AI coding tools are real, they are genuinely powerful, and they have changed how fast experienced developers can work. I use AI every day. For UI design, I use Stitch, which generates some of the best interfaces I’ve seen and converts them directly to code.
For boilerplate, for first drafts of components, for looking up syntax I haven’t used in months, AI is genuinely useful.
But here is what I discovered, and what I want you to really hear: AI is a multiplier, but only for people who already know what they’re doing.
Let me give you a specific example from my own work, because this is the kind of thing that doesn’t make it into YouTube videos.
One day, a section of my code was accidentally duplicated, the same logic block appearing twice in the codebase, creating a subtle but real bug. I turned to AI to help diagnose what was going wrong. And instead of reading the code, recognizing the duplicate in about thirty seconds the way any experienced developer would, the AI started writing new code.
Complex new logic to handle the edge case that the duplication was creating. It was generating code to fix a bug that was itself generating more bugs. It was treating the symptom instead of diagnosing the disease.
That’s a headache, specially for you, a time consuming process which makes you very frustrated. AI does not understand your codebase. It has no memory of the architecture decisions you made three weeks ago. It doesn’t know why you structured your authentication logic the way you did.
It cannot see the pattern of your entire project, because it can only work with what you put in front of it at that moment. And the larger and more complex your codebase becomes, the less useful that context becomes.
The longer your conversation with it runs, the harder it becomes for it to track the full picture of what you’re trying to build.
This is not an opinion. This is a hard technical reality of how these systems work. They operate within a context window, a finite limit on information. For simple, isolated tasks, this works fine. For large, complex, interconnected systems? AI gets lost.
It doesn’t know what it doesn’t know. And that gap, between what AI thinks it understands and what’s actually happening in your codebase, is exactly where the bugs live.
You will fix the bug eventually, but ask yourself: how long did that take? An hour? Two hours? A bug that someone with a solid understanding of the language would have spotted and resolved in ten minutes.
And this isn’t just my observation, a recent study found that developers who rely entirely on AI to build projects spend roughly 20% of their time building and 80% fixing what the AI broke. For developers who actually understand what they’re writing, that ratio flips completely.
The Myth That Almost Stopped Me (And Might Be Stopping You)
I said at the start that I almost quit. Now I want to explain what almost got me.
The narrative was everywhere: “Coding is dead.” “AI replaced developers.” “Claude Code just killed programmers”. I watched these videos. I read these posts. I felt the anxiety they were engineered to generate. And for a stretch, honestly, I doubted the path I was on.
Then I started paying attention to what was actually happening in companies instead of what was being performed on social media.
The companies that rushed to replace software engineers with AI tools thought they had found a shortcut. Fire the engineers, pay for tokens, ship faster at lower cost. The logic seemed airtight on a spreadsheet.
Take Oracle. In 2022, they acquired Cerner, a healthcare technology giant, for $28.3 billion, absorbing 28,000 employees who understood those legacy medical systems inside out. Oracle needed those engineers to migrate decades of complex healthcare data to their cloud infrastructure.
Once the data was safely migrated and the integration complete, Oracle sent a 6:00 AM automated email to 30,000 global workers on March 31, 2026, terminating their employment instantly. The public narrative blamed AI automation.
The financial reality was colder: Oracle calculated that firing those workers would free up $10 billion, money they needed to buy Nvidia GPUs and service the debt from the acquisition itself. They used the people to get what they wanted, then discarded them.
And yet, the broader industry data tells a more complicated story. According to research compiled by Robert Half, 29% of companies that eliminated roles due to AI have already rehired for those exact same positions, with more than half reversing that decision within six months.
Gartner predicts that 50% of companies that replaced technical staff with AI will be forced to restaff under different titles. The average company that replaced developers with AI is now spending $1.27 for every $1.00 they saved.
The “AI Boomerang” is real. And it exists because software is not just about writing code.
Corporate leaders have started publicly acknowledging what the data already showed: AI is excellent at generating syntax. It is not capable of thinking. And thinking is what software development actually requires.
What AI Has Actually Changed, And Why It Benefits You
Here’s my honest view of AI’s real impact.
AI has raised the floor of what a single skilled developer can accomplish. The boilerplate that used to eat hours of your week now takes minutes. First drafts of components appear instantly. Documentation gets easier to navigate. A developer with strong fundamentals can now output work that previously required a larger team.
Think of it this way. If you handed a junior architect a world-class drafting tool, they still cannot design a structurally sound building without understanding load-bearing principles. The tool accelerates execution, but understanding is still yours to develop.
I’ve watched this split happen in real time. On one side are developers who understand systems, who can look at AI-generated code and immediately identify the architectural flaw that will cause problems at scale.
On the other side are what people have started calling “vibe coders”, people who have AI generate entire applications, deploy them without understanding what’s inside, and then have no idea what to do when something inevitably breaks in production.
I’ve been in real, professional situations where the path forward required me to reason about a system that AI couldn’t see the full picture of. The answer required judgment that only comes from having built and broken and fixed real things. That judgment is the thing worth developing.
The Real Skills That Will Define Your Career
If I had to compress everything I’ve learned, from that first Python certificate to leading engineering, into what actually determines long-term career success in software, here’s what I’d say.
Think in systems, not functions.
The most valuable thing you can develop is the ability to see how all the pieces of a software system interact. How does data flow from a user action through the frontend to the API to the database and back? Where are the bottlenecks? Where do things break under pressure?
You don’t need to be an advanced developer to start thinking this way. Even when building small projects, ask yourself why you’re structuring things the way you are. That habit, built early, is what eventually makes the difference between someone who can write code and someone who can engineer a product.
Learn to diagnose, not just fix.
Debugging is the most undervalued skill in software development and arguably the one that matters most.
The ability to trace a problem from its symptom back to its root cause, through layers of abstraction, across services, through logs and error messages and unexpected edge cases, is something that only comes from practice.
From sitting with a bug you don’t understand, refusing to give up, and eventually finding it. Every hour I spent stuck on a problem I eventually solved taught me something permanent.
Develop product thinking.
Understand what a feature is supposed to accomplish before you design it. What happens when it fails? Who uses it and what do they actually need? How does it connect to the business’s goals?
This kind of thinking is what takes developers from “someone who writes code” to “someone who builds things that matter.”
Every project I’ve shipped that had real impact, the e-commerce intelligence system that eliminated 28 hours of manual work per week, the legal AI system that gives users access to legal information, worked because I understood the problem before I wrote a single line.
Communicate clearly.
As I moved from intern to junior to associate engineer, the thing that accelerated my growth most, outside of technical skill, was learning to articulate technical decisions to non-technical people. To explain why an architecture choice matters.
To present a tradeoff clearly. To conduct a code review that teaches rather than just criticizes. Communication is a professional skill. Treat it like one.
Fear Is Not the Right Response. Here’s the Right One.
I want to speak directly to something I see everywhere in developer communities right now: a generation of potential developers who are completely paralyzed.
They’re interested. They want to learn. But they’ve been so thoroughly convinced that AI is going to make their skills obsolete before they’ve even developed them that they haven’t started. They’re waiting for the dust to settle. For the debate to resolve. For someone to give them permission.
That paralysis is the real career threat. Not AI.
Every major technological shift in history produced exactly this fear. When calculators arrived, people said mathematicians would become obsolete. When the internet arrived, businesses said their industries would collapse.
When cloud computing emerged, server engineers were supposed to be finished. In every case, the people who learned to work with the new technology thrived. The people who refused to adapt, or who waited too long to start, are the ones who actually suffered.
The developers who thrive in the AI era are the ones who developed themselves deeply enough to use AI well and correct what it gets wrong.
My Honest Outlook
I’m genuinely excited about where software development is going. I see challenges clearly, and the opportunity too.
The ones emerging from this era as the strongest are the ones learning to combine genuine human expertise with AI capability. I’m building toward that future every day.
Every system I architect, every production bug I diagnose, every code review I conduct teaches me something that compounds over time. That compounding is what a career is built on.
If you’re just starting out, here is what I want you to know, clearly and directly: you are not too late. The fundamentals still matter. Problem-solving still matters. Systems thinking still matters.
And if you build that foundation now, while everyone else is either panicking about AI or blindly outsourcing their thinking to it, you will be exactly the kind of developer the industry is trying hardest to find.
The Last Thing I’ll Say
Start. Not when you feel ready. Not when the debate about AI settles. Not when the perfect roadmap reveals itself. Start now, with whatever you have, wherever you are.
Learn the fundamentals of your language until you own them. Build something small and break it on purpose. Fix it and understand why the fix worked. Then build something bigger. Solve problems every day until pattern recognition becomes instinct.
Use AI as a collaborator, not a replacement. Let it handle the tedious so you can focus on the thoughtful. When AI gives you code you don’t fully understand, don’t just run it, read it, question it, understand every line. Because the moment something breaks in production, and it will, you need to be the one who can figure out what happened next.
I remember the day I built my first complete website from scratch. No template. No AI generating the structure. Just me, a blank file, and everything I’d forced myself to learn by doing it the hard way. It took longer than it would have with AI. It was harder. But when it was done, I understood exactly why every line worked.
That understanding is the thing nobody can take from you. Not a layoff. Not an AI model. Not a trend cycle.
The tools will keep changing. Frameworks will rise and fall. New languages will emerge and old ones will evolve. But the ability to think clearly, build systematically, and solve problems under pressure, that never goes out of demand.