My niece recently finished her first year of pre-med – put in the hours, did the labs, memorized stuff (tried), and hated it. Not just the workload, but the whole idea of spending years in med school for a career she doesn’t know for sure that she wants.
So she’s shopping for a new direction, and after talking to her guidance counsellors and exploring a bunch of industries, she still doesn’t LOVE any of her options… but she’s decided to (eeny meeny miny…) choose a path she can tolerate and just finish that degree.
Cue her bestest most helpful auntie’s advice. I told her:
If you’ve got the braincells to handle higher education (she does) and you just want to add a tertiary qualification to your resume, go for the thing that can make you a versatile employee, the skill that is in demand across all industries, the expertise with the potential to earn you the most money.
The very next week, she enrolled in Computer Science.
And yes, I told her that even in the face of the AI revolution which, I know you know, is threatening to make coding and programming obsolete. Why?
Here’s my story.
I am a self-taught web developer who recently – finally! – went back back to school to complete a software development degree. And in that same year, ChatGPT dropped, kicking off one of the biggest disruptions in tech since the internet initially hit college campuses way back during my first attempt at university (yes, I’m that old).
Next minute, AI is generating everything from homework to code and getting better at it – exponentially – every day, so of course now we’re asking: Is programming still worth learning? Will AI replace software engineers? Is my bright idea to start a software development degree a poorly timed mistake? Even major tech CEOs are suggesting that, thanks to AI, if we never learn a line of code, “everyone is now a programmer.”
So, as a nerd with some concerns about her future in tech, I spent the next year and a bit (on top of my all my homework assignments) bingeing AI content, testing AI automation tools, playing around with no-code platforms, and building my own Python-based AI scripts… and here’s my take.
AI is not replacing programming. It’s changing it.
And if you understand how software actually works, you’re not becoming obsolete—you’re becoming essential.
Let’s talk about why.
The Evolution of AI in the Tech Industry
If it feels like AI took over the world overnight, that’s only because it finally became usable. But AI has actually been around for decades.
In 1956, a group of researchers at Dartmouth College coined the term artificial intelligence, setting the foundation for what would become one of the most transformative fields in tech.
Some key moments since then:
- 1958 – The perceptron, one of the first neural networks capable of learning, was introduced.
- 1966 – ELIZA, an early chatbot, showed that computers could simulate conversation (sort of).
- 1997 – IBM’s Deep Blue defeated chess champion Garry Kasparov, proving AI could outthink humans in specific areas.
- 2016 – Hanson Robotics introduced Sophia, a humanoid AI that blurred the lines between robotics and intelligence.
For most of its history, AI was a background technology—used in research labs, data analytics, and specialized applications. But that changed in 2022 when OpenAI launched ChatGPT.
What made ChatGPT different? It wasn’t just powerful—it was accessible. Suddenly, anyone with an internet connection could generate text, write code, and automate tasks. It turned AI from a niche tool into something that millions of people could use daily. And that’s when the real shift happened.
Now, AI-powered coding assistants like GitHub Copilot, Claude, and Gemini (formerly Bard) are making programming faster, leading some to believe that coding will become obsolete. Even NVIDIA’s CEO, Jensen Huang, suggested that AI will make programming so intuitive that “everyone is a programmer now.” That kind of statement fuels a lot of speculation.
But here’s the reality—just because AI can write code doesn’t mean it understands software development. AI can generate snippets of code, sure. But can it design a scalable system? Debug complex issues? Architect an entire software product from scratch? Not really.
Not yet, anyway.
AI is a tool. A powerful one, but still a tool.
Impact on Employment
Of course, AI is changing the job market. In fact, IT unemployment recently rose from 3.9% in December to 5.7% in January. That’s not only because of AI, but it’s part of a broader shift in how tech jobs are evolving.
And that’s the key—jobs aren’t disappearing, they’re changing. AI is automating repetitive coding tasks, but companies still need people who understand how software works. Developers who know computer science fundamentals—problem-solving, data structures, system design—are still in demand.
And those who learn to work with AI, rather than fear it? They’re in the best position of all.
Let’s get into that next.
The Enduring Importance of Coding Skills
As technology becomes more AI-driven, coding skills aren’t just about knowing the syntax of Python, JavaScript, or any other programming language. Programming is fundamentally about understanding how a computer processes information so we can give it structured instructions and get it to perform powerful tasks for us. It’s this ability to think like a programmer—breaking down complex problems, designing logical workflows, and structuring data efficiently—that will keep software developers indispensable.
AI can generate code, but it can’t think critically about why something needs to be built a certain way or how it should serve human users. That’s where we come in.
AI as a Tool, Not a Replacement
Take Alex, a software developer building an e-commerce platform. He’s using an AI coding assistant to handle the busy work—generating boilerplate code for user authentication, setting up a database schema, and even autofilling some common API calls. AI is excellent at cranking out this kind of repetitive, structured code much faster than a human can.
But Alex isn’t just writing code—he’s orchestrating the entire build. His real job isn’t about typing out function definitions; it’s about translating the needs of real human users into instructions a computer can work with. He has to design an intuitive checkout process, make sure the recommendation engine personalizes product suggestions in a way that feels natural, and structure the database so it scales efficiently as the store grows.
AI can suggest code snippets, but it doesn’t understand the end goal. It doesn’t know how users behave, what makes an interface intuitive, or how to balance security with usability. That’s all on Alex. The AI is an assistant—it accelerates development, but it doesn’t replace the developer.
Foundational Understanding of Computer Science
At its core, computer science is about how we organize and process information to solve problems efficiently. Two of the most fundamental concepts in programming are data structures and algorithms—and if you understand these, you understand how computers think.
- Data structures are the way we store and organize data so that a computer can access and manipulate it efficiently. Think of them like different types of filing cabinets—some are great for quickly looking things up (like a dictionary), while others are better for organizing tasks in order (like a to-do list).
- Algorithms are the step-by-step instructions a computer follows to process data and solve a problem. If data structures are the filing cabinets, algorithms are the rules for how to retrieve, sort, and use the files inside them.
Why does this matter? Because how we structure and process information directly affects how AI systems make decisions—including the ethical ones.
Take autonomous vehicles, for example. These cars rely on trajectory planning algorithms to decide the safest course of action in real time. Let’s say an autonomous vehicle is driving down a city street when a pedestrian suddenly steps off the curb. The car’s sensor data (which is stored and processed using data structures) tells it there are three possible choices:
- Brake hard and risk getting rear-ended.
- Swerve left, but that could put a cyclist in danger.
- Swerve right, where there’s an empty sidewalk.
The trajectory planning algorithm analyzes these options in milliseconds, weighing risks and prioritizing safety. But who decides which choice is the “right” one? That’s not up to AI alone—it’s up to the engineers and ethicists who design these decision-making systems.
If an algorithm is trained on biased or incomplete data (for example, if it’s never tested in neighborhoods with lots of cyclists), it could make flawed or unsafe decisions. This is why programmers with strong computer science foundations are critical—they structure the data, design the decision-making logic, and ensure that AI systems align with human values.
AI doesn’t create its own ethical framework; we do. And that’s why coding skills—beyond just syntax—are so important in an AI-driven world.
These scenarios illustrate that while AI tools significantly enhance efficiency by handling routine tasks, the role of human developers remains crucial. Complex problem-solving, ethical decision-making, and creative innovation are domains where human intelligence and a solid foundation in computer science continue to be indispensable.
Adapting to New Technologies
Continuous Learning and Skill Development
The one thing you can’t do in tech? Get too comfortable. Software engineering has never been a static field, and with AI accelerating change, adaptability is more important than ever. If you want to thrive in an AI-driven world, you have to stay ahead of the curve.
- The skills employers look for in software engineers shift constantly—by more than 30% between 2015 and 2022 alone. (brookings.edu)
- New frameworks, languages, and methodologies emerge all the time, meaning the best developers are the ones who embrace lifelong learning—whether that’s through online courses, coding bootcamps, or just keeping up with industry trends.
- The ability to learn how to learn is now a core skill. Mastering one programming language is great, but knowing how to quickly pick up new ones is what will make you truly valuable.
This is exactly why coding isn’t going away. It’s evolving. AI may change how we write software, but at the end of the day, people who understand how software works will always be in demand.
AI as an Enabler for Developers
AI is shifting the way developers work, but not by making them obsolete—by making them more efficient. Instead of replacing programmers, AI is acting as a high-powered assistant that helps engineers build faster, smarter, and more complex systems.
- AI automates repetitive coding tasks—things like generating boilerplate code, debugging, and optimizing performance. This frees developers to focus on high-level problem-solving, system design, and creative innovation. (builder.io)
- With AI handling busywork, engineers can build more powerful applications. Instead of getting bogged down in syntax errors or writing the same functions over and over, developers can spend more time designing user-friendly interfaces, optimizing security, or scaling applications for millions of users.
- AI is a multiplier, not a replacement. Think of it like a power tool—sure, it makes the job easier, but you still need a skilled craftsperson to operate it. AI can assist with coding, but it doesn’t understand why something needs to be built a certain way. That’s the developer’s job.
The takeaway? AI isn’t making coding skills irrelevant. If anything, it’s making them more valuable—because the people who can harness AI effectively will be the ones leading the future of software development.
Conclusion: Keep Coding
The rise of AI has sparked a lot of debate about the future of coding, but here’s the truth: programming isn’t going away—it’s just evolving. AI tools like ChatGPT and GitHub Copilot can automate repetitive coding tasks, but they can’t replace the problem-solving, creativity, and deep technical understanding that skilled developers bring to the table.
We’ve covered a lot in this post, so let’s break it down:
- AI is a tool, not a replacement. It speeds up workflows, but it still needs human developers to structure projects, make architectural decisions, and ensure software serves real human needs.
- A strong foundation in computer science is more important than ever. Understanding data structures, algorithms, and system design is what allows developers to guide AI in ethical and useful directions.
- Technology is always changing—adaptability is key. The skills employers seek in software engineers evolve rapidly, and those who commit to continuous learning will always stay ahead.
- AI is making developers more powerful, not irrelevant. With AI handling repetitive work, programmers can focus on bigger challenges—building more complex, scalable, and user-friendly applications.
The bottom line? Coding is still one of the most valuable skills you can learn. Whether you’re just starting or already deep into your programming journey, the best thing you can do is keep learning. The future belongs to those who understand how technology works—and who are willing to evolve with it.
So, if you’re ready to sharpen your coding skills, explore the world of AI-powered development, or just need a little inspiration along the way—stick around. Follow Guava Geek for insights, tutorials, and a front-row seat to the future of tech. 🚀
