The first time I used an AI assistant to fix a gnarly bug, I felt two things at once: relief that it was solved in four minutes, and a small, nagging sense that I had been robbed. Not by the tool. By myself. Two weeks later the same class of bug showed up in a different file, and I stared at it like a stranger. The AI had solved the problem. I had learned nothing.
This is the part of the AI conversation that gets flattened into slogans. One camp says friction is waste and anything that removes it is pure gain. The other says we are raising a generation that cannot think. Both are too neat. The honest position is that some struggle is where understanding gets built, and some struggle is just pain with no payoff, and the whole skill now is telling them apart.
Not all friction is the same
There is a concept from learning research called desirable difficulty. The idea is that certain kinds of effort, retrieving something from memory instead of rereading it, working a problem before seeing the solution, make the knowledge stick harder precisely because they were hard. The difficulty is not a tax on learning. It is the mechanism of learning. When you struggle to recall how a data structure works and then get it right, you are not being inefficient. You are laying down the thing that lets you recall it next time without help.
Now compare that to the friction of fighting a badly documented API for an afternoon, or hand-formatting a table, or writing the same boilerplate for the tenth time. That friction teaches you nothing you did not already know. It is the pain of a task, not the effort of learning. Handing it to a machine is an unambiguous win, and anyone romanticizing it has confused suffering with growth.
The trouble is that these two kinds of friction feel identical in the moment. Both are annoying. Both make you want the answer now. And the AI is equally happy to dissolve either one. So the tool that saves you from pointless boilerplate is the same tool, with the same one-keystroke ease, that saves you from the productive struggle that would have made you better. It does not know the difference, and if you are not paying attention, neither will you.
The muscle you do not know you are losing
Here is what worries me, and I say this as someone who loves these tools and uses them all day. The loss is invisible while it happens. You do not get a warning that says your debugging intuition is atrophying. You just notice, months later, that you reach for the assistant a beat earlier each time, that the threshold of difficulty at which you give up and ask has quietly dropped. The struggle you skip does not announce itself. It shows up as an absence, later, when the tool is not there or is confidently wrong and you have lost the ability to check it.
There is a real professional risk in this. The senior engineer who is valuable is valuable because of ten thousand small struggles that built judgment. If the next cohort skips those struggles, they arrive at senior titles with junior instincts, and they will not know it, because the code shipped and the tests passed the whole way up.
A rule I actually use
I have started asking one question before I hand something off: will I need to understand this again? If the answer is yes, if this is a domain I work in, a system I own, a skill I want, I do the struggle first and let the AI check my work or fill the gaps. If the answer is no, if this is a one-time chore in a corner of the world I will never revisit, I hand it over without a shred of guilt and move on with my afternoon.
This is not a call to suffer for its own sake. Suffering for its own sake is a religion, not an engineering practice. It is a call to be deliberate about which difficulties you outsource, because the machine will take all of them if you let it, and a few of them were the point.
I still fixed that bug in four minutes. I would do it again. But now, on the problems I actually care about getting good at, I make myself sit in the not-knowing a little longer before I ask. It is slower. It is also the only part of the work that is still making me better, and I am not ready to give that up for a faster Tuesday.