In everything, there’s this mechanical feeling—a patterning you almost never think about, unless you force yourself to. Life, it turns out, is governed by these little, unnoticed forces and movements that pile up in the background, unremarkable until you pull them out into the light. You wake up, turn keys, hit buttons, grip pencils, write emails, or move your pinky finger just the right way to hit a semicolon. There’s muscle memory embedded in these actions, automatic and precise. These moments get absorbed into the machinery of your body, performed on autopilot.
When AI slides into the picture, you get this sudden rush of speed, this acceleration of things you didn’t even realize were slowing you down. It’s not that you’re skipping the work entirely; it’s that the work is different. You’re flying through prmopts and instrucitons, never pausing to shift your hand to reach that semicolon because it’s already there, filled in by the machine. Where you were a laborer, now you are a manager. The zenith inevitably comes: At some point you’re not even doing the thinking. You’re not doing the real work. But in reality, you’re just not doing the dumb, rote, mechanical bits anymore—the finger-shifting choreography of hitting a key combination over and over again. You’re skipping the repetitive muscle memory, but you’re still doing the thinking, still orchestrating. It’s just a higher-level thinking, a zoomed-out version of what you were doing before, with all the small tedium taken care of.
So when you get back to doing the work, the experience is like returning to a musical instrument after a long break. You pick up your bass guitar, and the slowness sets in—the rusty, jittery slowness of fingers re-learning where they’re supposed to go. You’re not just recalling intellectually what the notes are, or even how to move from E7 to A to F# minor in a way that’s elegant and fits within some long-forgotten fragment of music theory you might’ve learned. No, it’s much more physical than that. It’s buried in your hands, in the spaces between your muscles.
That gap between your thumb and index finger starts to throb—the thenar eminence—a name for the fleshy part of your hand that gets sore after holding the bass neck for too long. After two or three hours of practice, there’s the extensor pollicis longus, a muscle you probably didn’t even know existed until it ached, holding your thumb in place on the strings. The soreness hits the next day, a reminder that your body hasn’t done this in a while and needs time to catch up. It’s a physical ache, not just mental. Muscle memory isn’t just in your brain; it’s in the literal fibers of your hands, a learned thing your body has to remember through repetition and practice.
The same is true when you step onto a basketball court. You can read all the books on Pythagorean theory, Newtonian mechanics, and kinematics that you want. You can sit in a classroom, learning how the perfect arc of a shot is determined by the planar distance between you and the hoop, factoring in your height, reach, and the height of the hoop itself. You could calculate the ideal trajectory using all the math and physics available, and still not make the shot. Why? Because shooting a basketball isn’t just about understanding the theory behind it. It’s about repetition, about feeling the ball in your hands, about getting the muscle memory to the point where you know, instinctively, from almost anywhere on the court, exactly how hard to throw and how high to arc the ball. You build that knowledge by shooting again and again and again, thousands of times, until it’s no longer a conscious calculation but a reflex.
You don’t think about the mechanics anymore. You just shoot.
There’s a gap between knowledge and skill, between intellectual understanding and physical mastery, and it’s wider than most people realize. Mastery comes from doing, from reps, from the small mechanical actions your body learns over time. When you code with AI, you’re cutting out the small, learned movements—the semicolons, the indentations—but the bigger picture is still there. The orchestration, the higher-level thinking, remains. When you pick up an instrument, when you step onto the court, when you sit down to code, it’s the same. The mechanical is always there, somewhere deep beneath the surface, powering everything you do, whether you notice it or not.
But what happens when you use AI to do things you’ve never actually done? When you let the machine take over for something you’ve skipped entirely? The code is written, but you’ve never done the finger work, never wrestled with the syntax. The design is generated, but you’ve never opened a blank canvas and stared at it, trying to figure out where to start. You miss out on the reps, on the physicality of the thing, and as a result, you don’t develop that intuitive muscle memory—the kind that lets you know, on a gut level, what works and what doesn’t. Without that foundation, your dependence on the tool grows, and your understanding stays shallow. You’re not building the skill; you’re bypassing it. This is not necessarily a bad thing, but it’s important to understand the trade-offs.