This technological shift prompts an essential question: Is it worth learning to program when AI can do it for us? Are universities addressing this challenge properly? (View Highlight)
There is no simple answer, but the relevance of the questions shows just how much the future of coding learning and developing is at a crossroads. It means we must consider the role of learning in an era where writing code is no longer exclusive to humans. A major stumbling block is the belief that programming does not now give individuals an edge. If a model can produce in minutes what a programmer would take hours to do, why spend years studying something that a machine can do better and faster? (View Highlight)
The idea that anyone can now create working software without years of training is pretty tempting. Where is the motivation to convince the decision-making tool in my skull to make the effort, being as efficient as it is, as well as influenced by instant rewards? (View Highlight)
But hang on. This quick-fix thinking misses something crucial about what’s really happening in software development. Sure, AI can churn out code like nobody’s business, but that doesn’t mean human programmers are headed for irrelevance, or extinction. And what’s more important, that doesn’t mean that stopping learning how to code is the way to a better future. (View Highlight)
Coding and programming are usually confused. Programming isn’t just about writing lines of mysterious text. It’s about solving problems by understanding the language of technology. Yes, AI can write code, but if the human on the other end doesn’t know the basics, doesn’t have the context, how can they judge whether what the AI has produced is any good? Secure? Efficient? Ethically sound? (View Highlight)
Take a junior developer using AI to generate SQL queries. If they don’t recognize that the code lacks proper sanitization, they might be unwittingly opening the door to SQL injection attacks. Talk to any seasoned developer and they’ll be able to recount stories of junior team members blindly using AI-generated code only to crash spectacularly until rescued by someone who understands what’s going on under the hood. (View Highlight)
A true understanding of coding matters in ways that are not immediate – because when you learn to program, something changes in how you approach problems. This is not unique to coding of course, the same thing happens when you learn, for example, a new language, mathematics, or musical composition. It’s not just about writing code; it’s about developing a programmer’s mindset. Technical skills matter, of course, but it’s the mental transformation that might be most valuable in the long run. (View Highlight)
The process of learning to code rewires your brain in ways that pay dividends far beyond the technical ability to write code:
• You start breaking impossible-looking problems into bite-sized chunks.
• You develop a step-by-step mental discipline that cuts through complexity.
• You see connections between different systems that others miss.
• You create efficient solutions that can be applied again and again. (View Highlight)
There is no doubt, learning to program remains essential even as AI generates code. Without that foundational knowledge, these new tools will be of little value to us. Programming and coding are not dying – they are evolving. This means the way we teach and learn this discipline must also change. Rather than memorizing syntax, the focus should shift toward understanding core principles and learning to collaborate with AI. (View Highlight)
The relationship between programmers and AI is more nuanced than simple replacement – it’s not as simple as handing over the programming reins. For example, Ethan Mollick’s research has found that while people initially get more done with AI tools, their productivity and the quality of their work decreases after relying on the tools for a while. (View Highlight)
The AI revolution won’t kill programming jobs – it’ll transform them into something new. AI Integration developers, AI/ML Engineers, Synthetic Data Engineers, AI DevOps Engineers. These new roles will need both technical chops and AI fluency – a uniquely human combination that no algorithm can match. (View Highlight)
So, to hammer in the point: learning to code in the age of AI is far from wasted effort. It’s an investment in becoming intellectually prominent in a digital world. Skipping programming because AI can do it for you is like refusing to learn math because calculators exist. Calculators didn’t kill mathematics – they elevated it. Similarly, AI won’t replace programmers, it’ll supercharge the ones who know how to use it. Tomorrow’s coders will wield AI like a power tool, focusing their uniquely human talents on the parts where judgment, creativity, and ethics matter most. (View Highlight)
