For as long as software has existed, there have been promises, often grand, sometimes naive, that the need to “know how to code” would soon vanish. The vision: ordinary people, business analysts, or even executives designing powerful applications without writing a single line of code. From the earliest days of computing to today’s AI revolution, this dream has been revived again and again. Yet, despite billions in investments and waves of hype, the core of software development, the logic, structure, and abstraction, remains stubbornly human.

The 1960s: COBOL and the Business User

In the 1960s, COBOL (Common Business Oriented Language) was created to make programming accessible to business people. With its English-like syntax, COBOL was supposed to bridge the gap between domain experts and machine code. The dream was clear: managers and analysts would write software themselves.

But COBOL, while more readable than assembly, still required training, structure, and logical thinking. The dream didn’t materialize. COBOL coders,still in demand decades later, became their own specialized workforce. Instead of removing the need for programmers, COBOL expanded the profession.

The 1980s-90s: 4GLs and Visual Tools

Fourth-Generation Languages (4GLs) promised another leap. Tools like Fox Pro, Power Builder, and Oracle Forms let users “draw” applications. Visual Basic allowed developers to build GUIs with drag-and-drop components. At the time, these were seen as the end of traditional coding.

But while these tools simplified UI creation and database binding, complex business logic still required real coding. The abstraction broke down quickly as projects grew. Power users emerged, but professional developers remained essential.

The UML Era: Modeling as Programming?

In the late 1990s and early 2000s, the Unified Modeling Language (UML) was heralded as the new foundation for software development. Why write code, the thinking went, when you could diagram it? With Model-Driven Architecture (MDA), one could draw class and activity diagrams and automatically generate applications from them.

Despite heavy support from enterprise vendors, this approach never took off at scale. Software is not just structure; it’s behavior, and behavior is messy. Diagrams became too complex, brittle, and incomplete to replace real code. UML found a niche in documentation and architecture, but the coder was not dethroned.

The No-Code/Low-Code Renaissance

In the 2010s, a new generation of no-code and low-code platforms emerged: Bubble, Out Systems, Mendix, and others. These platforms boasted intuitive interfaces for building web apps, workflows, and integrations. This time, the audience expanded to entrepreneurs and startups.

While successful for prototyping, internal tools, or constrained domains, these platforms hit a wall when it came to scalability, customization, and maintainability. Developers were still needed to extend functionality, ensure security, and keep performance in check. Once again, the promise remained only partially fulfilled.

Now: AI Will Replace Coders?

The latest iteration of the promise centers around artificial intelligence. Tools like GitHub Copilot, ChatGPT, and Claude can write code, refactor it, explain it, and even suggest solutions. Surely now, many claim, AI will finally eliminate the need to know how to code.

But even AI doesn’t remove the core challenge of software development: understanding what needs to be built, translating that into logical structure, and debugging edge cases. AI is a powerful tool—perhaps the most powerful yet—but it is a copilot, not a captain. It accelerates developers, it doesn’t replace them. Just as calculators didn’t eliminate the need to understand math, AI won’t eliminate the need to understand code.

Why the Dream Won’t Die—and Why It Won’t Come True

The repeated promises share a common mistake: underestimating what software development actually is. Coding is not just syntax; it’s problem-solving, system design, abstraction, trade-offs, and communication. Each time we try to automate or abstract it away, we rediscover how central human reasoning is to the process.

Software is not a commodity product. It’s a living, changing expression of intent. Until we can automate intent, and all the ambiguity, creativity, and complexity it entails, there will always be a place for coders.

Technology is not inherently harmful. Search engines and AI can be powerful allies in learning and productivity. But when they replace thinking rather than enhance it, they become crutches rather than tools. The challenge of our time is to teach the next generation to use technology without losing the ability to think independently. Encouraging critical thinking, deep learning, and cognitive struggle, yes, even struggle, is essential.

Ultimately, intelligence is not about having access to the right answers. It is about knowing what questions to ask, and having the discipline and skill to explore them on your own. If we lose that, we risk not just a decline in IQ, but a decline in what it means to be truly human.

In an age of unprecedented technological progress, the world has become smarter, or so it seems. With the advent of search engines and now AI chatbots, information is no longer something we must store in our minds but something we summon instantly with a few keystrokes or a spoken prompt. On the surface, this transformation appears to be the pinnacle of human advancement: infinite knowledge at our fingertips, answers without effort, and decisions made in seconds. However, beneath this veneer of efficiency lies a growing concern one that educators, psychologists, and sociologists are beginning to voice more openly: the potential long-term decline in human intelligence, especially across generations, driven by our increasing dependence on machines to think for us.

From Memory to Machines: The First Phase of Intellectual Outsourcing

The shift began subtly with the rise of the internet and the dominance of search engines like Google. Suddenly, it became unnecessary to memorize historical dates, learn formulas, or even know how to spell difficult words. Why bother when the answer is only a search away? While this democratization of information broke down barriers and made learning more accessible, it also quietly redefined the nature of knowledge acquisition. The emphasis shifted from understanding to retrieving.

This transformation had a psychological cost: if information is always available, the incentive to internalize it weakens. Attention spans shortened, critical thinking skills eroded, and the depth of understanding gave way to a reliance on surface-level summaries. Studies began to show that people were becoming less likely to remember facts they could easily look up.a phenomenon known as the “Google Effect.” The first signs of cognitive atrophy were already visible.

Enter AI: The Second Phase of Intellectual Dependency

Just as society adjusted to search engines, AI chatbots arrived and elevated convenience to a new level. These tools don’t just retrieve information, they process, synthesize, analyze, and even make decisions on our behalf. Whether it’s choosing a workout plan, composing a thoughtful message, solving a math problem, or making a complex business decision, AI now offers personalized, immediate assistance that often bypasses the need for human deliberation altogether.

For the younger generation, raised in a world where AI is as natural as electricity, the temptation is enormous: why struggle to think through a problem when an AI can solve it faster and better? Why read the whole book when a chatbot can summarize it in seconds? Why develop a nuanced opinion when a bot can simulate one for you?

The Illusion of Intelligence and the Decline of Autonomy

This dependency has a deeper consequence than simple intellectual laziness, it fosters a growing inability to be cognitively autonomous. Increasingly, young people are showing signs of being less equipped to form their own judgments, solve unfamiliar problems without assistance, or think deeply about abstract concepts. If every question is answered for you and every choice optimized by an algorithm, when do you develop the muscles of independent reasoning?

Moreover, this trend can lead to a diminished sense of responsibility for one’s knowledge and decisions. When AI handles the cognitive heavy lifting, humans become passive participants in their own intellectual lives. The risk is not just lower IQ scores, but the erosion of the skills that IQ was once a proxy for: reasoning, memory, learning, and decision-making.

The Dangerous Comfort of Convenience

Convenience is addictive. When getting answers is easy, learning feels unnecessary. When decision-making is delegated, discernment atrophies. What’s worse, this decline is self-reinforcing: the less we use our cognitive faculties, the less capable we become of using them. Over time, a generation raised in the comfort of artificial intelligence may wake up to find that while the tools have grown smarter, they themselves have grown less so.