The Hidden Cost of Skipping the Fundamentals in the Age of AI
The Problems of Shallow Learning
One profound shift I’ve observed recently is how AI has made it incredibly easy
to start using new technologies without really learning them in depth or
sometimes even without learning them at all. While this approach can feel
like a productivity boost, I believe it’s a ticking time bomb that may become
disastrous in the long run.
Not long ago, I was working with a colleague to improve a specific part of a
Retrieval-Augmented Generation (RAG) system I had built. To explain my
approach, I began with a simple, toy example consisting of a few lines of text
meant to test and validate the core idea. To my surprise, my colleague ignored
the toy example entirely and insisted on jumping straight to the full-blown
complex dataset, disregarding the foundational illustration that, in my view,
was crucial to grasping the underlying concept.
I have noticed this pattern repeatedly, whether in pair programming or
technical discussions with peers. It seems to me that AI has enabled a tendency
to leap directly to a “working” solution, treating the understanding of
concepts as an unnecessary “low-level implementation detail” that can be
skipped since “AI will handle the boring tasks.”
Why Real Learning Still Matters
“Learning,” at least in my experience, is inseparable from understanding.
Breaking down concepts into their most basic, atomic parts, isolating them to
specific problems is the prefferred way to truly grasp them.
A top-down approach—immediately jumping to a working solution, often with AI’s
help can lead to a “working solution”, but ignoring the underlying concepts and
details that wait to be understood is a recipe for disaster and a terrible way
to learn.
Developers, managers, and companies alike should recognize this risk. No matter
how powerful our tools have become, there are still things that require patience,
time, and genuine effort to master. Skipping these steps isn’t just a personal
shortcoming; it can become an organizational liability.
The Cost of Shallow Solutions
Working solutions created with limited understanding are often fragile,
difficult to extend, and hard to maintain. The lack of foundational knowledge
reveals itself in messy codebases and architectural mistakes that become
expensive (or impossible) to fix later.
Based on what I’ve seen, this habit of jumping straight to the “full-blown
solution” will prove very costly over time. I genuinely believe entire
companies can find themselves in crisis if they reward speed and surface-level
results over understanding and craftsmanship—especially if management mistakes
“getting things done faster” for real progress.
Don’t Blame AI— But Use it Responsibility
To be clear, I’m not against AI. Far from it—I think AI is an incredible tool
that can help us work smarter and more efficiently. I use it myself every day.
But we must be vigilant: we can’t let AI (or any tool) replace the hard work of
learning and understanding the technologies we use. Otherwise, we risk falling
into the trap of deploying low-quality solutions that may save us time today,
but will cost us dearly tomorrow.
