Introduction: We’re living in a time when technology can write code, generate art, and even answer questions that once required years of training. But as automation and AI take over more of our tasks, a quiet shift is happening beneath the surface: we’re doing fewer of the small things that once taught us how to think. That’s not just a technical concern—it’s a human one. This article explores how small, often-overlooked tasks help build deep understanding, and why letting go of them too quickly might come at a cost.
Small Tasks and the Foundation of Expertise: Think about tying your shoes or playing a chord on a guitar. At some point, those actions were hard. Over time, they became automatic. That’s procedural memory—one of the brain’s key ways of learning. It’s not about facts or concepts; it’s about knowing how to do something through repetition and practice.
In programming, this shows up in subtle but powerful ways. An experienced developer doesn’t just remember syntax—they instinctively sense what might break, what’s elegant, and what feels “right” in a given context. This intuition comes from years of doing the small things: debugging by hand, writing tests, refactoring messy code. Each of those tasks strengthens neural pathways, forming a kind of mental muscle memory.
But here’s the catch—just like muscles, those skills can atrophy. If we stop practicing them, they fade. It’s not just about gaining expertise—it’s about keeping it. When we hand over too many tasks to AI, we risk letting those hard-earned intuitions slip away. Tools can make us faster, but only if we stay engaged enough to maintain the understanding beneath the surface.
The Hidden Risks of Dependence: Let’s take game development as an example. Tools like Unity or Unreal Engine are brilliant—they let small teams build complex worlds. But when everyone uses the same tools in the same way, something is lost. Fewer developers push themselves to understand the logic behind what they’re doing. Fewer question the defaults. Innovation slows not because the tools are bad, but because the habits and mental muscles that lead to new ideas start to fade.
The same thing happens with AI. It’s tempting to let these systems handle the thinking for us—whether it’s writing, coding, or even decision-making. But when we outsource too much of the process, we also dull our ability to engage with the problem itself. We stop wrestling with complexity, and in doing so, we lose the deeper understanding that only comes from doing the work ourselves.
The Creative Power of Mastery: Paradoxically, doing the “boring” tasks is often what unlocks the most creativity. Musicians practice scales. Writers rework sentences. Coders read documentation and experiment. These small acts aren’t glamorous, but they shape how we think, and they give us the confidence to break rules when it matters.
This echoes a core idea from George Leonard’s Mastery book, where he describes true growth as a journey marked by plateaus, steady repetition, and a love of the process itself. Mastery isn’t about shortcuts—it’s about showing up and engaging with the work, again and again, even when progress feels invisible.
That’s why foundational skills matter so much. They’re not just about being thorough—they’re about expanding our mental models. Craftsmen who master the basics don’t just use tools—they build them, or find better ways to use them. That’s how innovation happens.
Conclusion: Automation and AI are here to stay, and they bring undeniable advantages. But the path to deep expertise—and real creativity—still runs through the basics. When we engage with small, hands-on tasks, we aren’t just working. We’re learning. We’re growing. And we’re making sure that the future we build with machines still has the best of what makes us human.