AI and engineers: real work doesn't vanish, it just hides better

AI has given us the illusion of shortening distances, but sometimes it just creates the illusion of work done. Think of when it gives you a perfect summary, yet you still need to read it to truly understand. It's not magic, just a very good assistant.

Every September 15th, India, Sri Lanka, and Tanzania celebrate Engineer's Day, a moment to remember the value of human ingenuity. Yet, with artificial intelligence, we're tempted to believe that design work has almost vanished. AI generates code, sure, but who integrates and understands it?

An AI assistant can churn out lines of code at crazy speeds. Sounds like a great advantage, right? The problem is, that code still needs to solve a real problem in a real context. AI doesn't grasp the nuances of your company or the complex interactions between systems.

It's a bit like asking a robot to cook a gourmet dinner. It'll give you ingredients and maybe even a recipe book, but the taste, plating, and experience come from you. The hardest part isn't writing the instructions; it's knowing which instructions to give.

Over-relying on AI for code can lead to nasty surprises. Your project might seem to be speeding along, but technical debt accumulates beneath the surface. Code generated without full understanding can become a nightmare to maintain and debug.

Imagine inheriting a project done this way. You'd end up with a bunch of code that "works," but nobody knows why it works or how it was put together. Every change becomes a risk, every bug a treasure hunt. Not exactly a recipe for productivity, is it?

This "convenience" can slow down long-term development. Instead of solving the problem once and for all, we find ourselves constantly fixing and adapting poorly thought-out solutions. AI is a crutch, not a brain replacement.

Absolutely. The human engineer is the one who defines the problem, designs the overall solution, and validates the final result. AI is a powerful tool, but it lacks intuition, critical thinking, or the ability to take responsibility for an error.

A good engineer knows that code is only part of the equation. They must consider architecture, security, scalability, and user experience. These are all things a language model, however advanced, cannot yet do on its own.

So, next time AI offers you a "turnkey" solution, remember that someone still needs to turn the keys. And that someone, thankfully, is still a human with a functioning brain.