The Quiet Death of Patience (And What It’s Doing to How We Build Software)
There’s a line I keep hearing from developers lately, usually said with a mixture of pride and mild embarrassment: “I can’t read documentation anymore.”
Not because the docs are bad. Not because they don’t have time. But because somewhere between the rise of GitHub Copilot, ChatGPT, and a dozen other AI coding assistants, the muscle that used to sit quietly and work through a problem — page by page, error by error — has started to atrophy.
And I think we need to talk about that.
The Friction Was the Feature
For a long time, the hardest parts of learning to code were also the most valuable. Spending three hours hunting a missing semicolon taught you to read your own code with forensic precision. Trudging through Stack Overflow threads from 2011 gave you context — you understood why a pattern existed, not just that it existed. The friction was slow, frustrating, and quietly building something inside you.
Now, that friction is almost optional.
AI tools have become so capable that a developer can go from idea to working prototype in an afternoon without deeply understanding a single line of the output. That’s genuinely remarkable. It’s also, depending on who you ask, either the most exciting thing to happen to software development in a generation — or a slow-moving crisis nobody wants to acknowledge out loud.
This Isn’t a “Kids These Days” Argument
Before we go further: this is not a nostalgia post. I’m not going to tell you to learn assembly language or insist that real programmers write everything in Vim. The tools we have now are extraordinary, and refusing to use them out of some misplaced sense of purity would be like a carpenter refusing a nail gun because they started with a hammer.
The question isn’t whether to use AI tools. The question is whether we’re being intentional about what we let them replace.
There’s a difference between using a calculator because arithmetic is beneath the task at hand, and losing the ability to estimate whether an answer is roughly correct. The first is efficiency. The second is a quiet loss of something you didn’t notice leaving.
What’s Actually Changing in Developer Culture
Talk to engineering managers at mid-size companies right now and a pattern emerges. Junior developers are shipping faster than ever. Their code often looks clean, well-commented, and follows modern conventions. But ask them to debug something unfamiliar without an AI assist, or to reason through a system design problem from first principles, and there’s a hesitation that wasn’t quite there before.
Meanwhile, senior developers are reporting something different: a strange kind of cognitive offloading that feels productive in the moment but leaves them vaguely unsettled. The deep, immersive focus that used to define a good coding session — the kind where two hours vanish and you surface with a genuine understanding of what you built — is getting harder to find.
We’re optimizing for output. We may be inadvertently de-optimizing for understanding.
The Attention Economy Already Did This Once
We’ve been here before, just with different tools.
When smartphones arrived, we didn’t immediately lose our ability to concentrate. It happened gradually — one notification at a time, one scroll at a time — until one day people found themselves genuinely unable to sit with a book for twenty minutes without reaching for their phone. The capability didn’t vanish overnight. It eroded.
The concern with AI coding assistants isn’t that they’ll make developers redundant (a conversation that is frankly getting boring). It’s that they’ll make a certain mode of thinking redundant — the slow, patient, generative kind that produces not just working code but deep expertise.
So, What Do We Actually Do About It?
A few things that seem worth trying — not as rules, but as deliberate choices:
Build something without assistance, regularly. Not everything. Not forever. Just one small project a month where you sit with the discomfort of not knowing and work through it the long way. Treat it like going to the gym for a part of your brain you don’t want to lose.
Read the code the AI gives you like it’s a suspect, not a colleague. Don’t just run it — interrogate it. What does this actually do? Why this approach and not another? Could this fail? Where? This habit turns AI from an answer machine into a teaching tool.
Reintroduce documentation as a first stop, not a last resort. Official docs are written by people who understand the system at a level that a summarizing AI model may not fully replicate. There’s texture in documentation that gets compressed away. Sometimes that texture matters.
Have the honest conversation with your team. If you’re leading developers, ask openly: are we building expertise, or are we just building features? The answer can be both — but only if you’re paying attention.
The Bigger Picture
The technologies that reshape how we work always reshape how we think. Word processors changed how people compose sentences. Search engines changed how people recall information. AI assistants are changing how developers solve problems — and that process is happening faster than our culture of craft has had time to respond.
That’s not a reason for panic. It is a reason for intentional.
The developers who will be exceptional a decade from now probably won’t be the ones who refused to use AI tools, or the ones who handed everything to them. They’ll be the ones who stayed curious about the machinery underneath — who used powerful tools without letting those tools do all the thinking.
Patience, after all, is not just a virtue. In software, it’s a skill.