Not long ago, coding felt like play 👾

We chased bugs like mini-bosses, hacked scrappy prototypes on weekends, and celebrated the elegance of a one-liner that replaced forty clumsy lines. The work was demanding, but it was also deeply engaging.

Today, AI is undeniably improving software development in real and important ways: speed, accessibility, test coverage, documentation, and onboarding. These gains matter.

Yet something more subtle may be at risk.

As AI takes on more of the "thinking" and "figuring out," some of the activities that made coding intellectually satisfying begin to flatten into button clicks and autocomplete.

The Sources of Joy in Coding ❤️

Hours disappear while you debug a gnarly race condition—until click… insight! That rush is earned.

Reading source code, stepping through a debugger, and spelunking logs teach us how systems actually behave, not just how we hope they do. You learn by engaging directly with the machinery.

Choosing data structures, naming things well, shaving off a few milliseconds in a hot path. These micro-decisions are the craft. They are how engineers express judgment, taste, and care.

How AI Can Dull the Edges 🔪

From exploration to suggestion acceptance
Autocomplete and code generation compress the path from idea to implementation, often bypassing the friction where understanding is built. Less grappling can mean less growth.

From debugging to prompt-tweaking
Instead of tracing failures to root causes, we nudge prompts to whip up a quick fix. The system works, but our mental model remains shallow.

From architecture to assembly
When AI proposes scaffolding early, abstractions can harden before we fully understand them, shifting the role from design to integration.

From craftsmanship to compliance
Lint, generate, format, ship. The requirements are met, but the pursuit of a sharper or simpler solution can fade under the weight of automation.

From shared language to outsourced thinking
Teams may converge on AI-generated patterns that no one fully owns, weakening code reviews and eroding shared understanding.

The Hidden Costs 💰

When AI becomes the first resort, core instincts atrophy. When it fails quietly, we may not notice until production does.

Junior engineers who skip the hard miles of debugging and refactoring miss the muscle-building that senior judgment depends on.

Models converge on average patterns. Average is rarely the bar for resilient, long-lived systems.

AI is a powerful tool. The question is not whether we use it, but whether we remain engaged thinkers and craftspeople while we do.