AI has made writing code ridiculously fast. You can go from idea to working feature in minutes. It feels like a superpower.
But there’s a catch nobody talks about enough.
While we’re speeding up code generation, we’re quietly slowing down understanding. And that gap? That’s what I call comprehension debt.
What is comprehension debt?
It’s simple:
The difference between how much code exists… and how much of it people actually understand.
And unlike technical debt, it doesn’t scream at you.
There are no broken builds.
No obvious mess.
Tests are green. PRs are merged.
Everything looks fine.
Until one day, it’s not.
The moment it hits
You’ve probably seen this before:
You try to make a small change…
Something unrelated breaks.
You fix that… and something else breaks.
Now you’re afraid to touch anything.
That’s not messy code. That’s missing understanding.
At some point, the team stopped knowing why things were built the way they were. The system still works—but nobody really gets it anymore.
That’s comprehension debt coming due.
A quick example from IFarmer
Let’s make this real.
Imagine a feature in Ifarmer Limited’s retailer app—like the Wheelspin (spin-to-win) campaign used for user engagement and rewards.
An engineer uses AI to quickly generate logic for:
spin eligibility (who can spin and when)
reward distribution (discounts, cashback, offers)
UI state handling (loading, success, retry)
Everything works. Tests pass. PR gets merged.
A few weeks later, someone tries to:
tweak reward logic
add a limit per user
or fix a small UI issue in the spin flow
Suddenly:
users get multiple rewards unexpectedly
some users can’t spin at all
or rewards don’t match what the API returns
Now the problem isn’t the bug.
The problem is:
Nobody fully understands how the feature was put together.
The original logic came from AI.
The reviewer skimmed it.
Edge cases (like retry, failure, duplicate spins) were never deeply thought through.
So now:
every change feels risky
debugging takes longer than writing the feature
and confidence in the system drops
That’s comprehension debt—right there in production.
AI made this easier to fall into
Here’s the core problem:
AI can write code way faster than humans can understand it.
That flips a long-standing dynamic in engineering.
Before:
Writing code was slow
Reviewing code was manageable
Review = learning + quality control
Now:
Code is cheap and fast
Review is overwhelmed
And a lot of code gets approved without deep understanding
Even worse—AI code looks good. Clean. Structured. Reasonable.
But “looks correct” ≠ “is correct in your system.”
The review bottleneck is gone (and that’s not good)
It used to be hard to get code into the system. That friction was useful.
Reading a PR forced you to:
Understand decisions
Catch weird assumptions
Learn how the system fits together
Now?
A junior dev with AI can generate more code than a senior can realistically review.
The bottleneck didn’t disappear—it just moved.
“We have tests” — not enough
The natural reaction is:
“We’ll just rely more on tests.”
And yeah—tests help a lot. You absolutely need them.
But they have limits.
Tests only check what you thought to check.
They don’t catch:
Edge cases nobody imagined
Weird UI bugs
Subtle behavior changes
And here’s a scary one:
AI can change code and update the tests to match it.
Now everything passes… but the behavior might be wrong.
At that point, the real question is:
“Should this change have happened at all?”
Tests can’t answer that. Only understanding can.
Specs won’t save you either
Another idea people love:
“Write detailed specs. Review the spec. Let AI implement it.”
Sounds clean. Almost perfect.
But in reality:
Specs can’t capture every edge case
Implementation involves tons of small decisions
Two people (or AIs) will interpret the same spec differently
Also, if your spec is detailed enough to remove ambiguity…
…it’s basically the code already.
The real shift: understanding is now the bottleneck
AI didn’t remove the hardest part of software engineering.
It just exposed it.
The hard part was never typing code.
It was understanding systems.
And now that code is cheap, understanding is the most valuable skill in the room.
The engineers who matter most are the ones who:
Know why things were built a certain way
Can spot risky changes instantly
Understand how everything connects
That’s the new scarcity.
The scary part: you can’t measure this
Here’s why this is dangerous:
All your metrics look great.
Velocity ↑
PR count ↑
Test coverage ✔️
Deployments ✔️
From the outside, everything is improving.
But comprehension?
Completely invisible.
And teams optimize for what they measure.
This will matter more soon
Right now, you can get away with not fully understanding AI-generated code.
But think about where this is going:
Healthcare systems
Financial infrastructure
Government platforms
When something breaks, “the AI wrote it” won’t be an acceptable explanation.
Understanding will become accountability.
So what do we actually do?
The goal isn’t “write more code faster.”
It’s:
“Understand what we’re building—deeply.”
That means:
Be clear about what a change should do before writing it
Don’t treat tests as a substitute for thinking
Actually read and question important changes
Maintain a mental model of the system
And most importantly:
Don’t confuse “it works” with “we understand it.”
Final thought
AI made code cheap.
But understanding?
Still expensive. Still manual. Still necessary.
If you skip it, you’re not saving time.
You’re just taking on debt.
And comprehension debt collects interest fast.
Written By
Muhaimnur Rahman
Senior Software Engineer
ifarmer Limited
#iFarmer #SofolApp #MobileAutomation #TestAutomation #AgriTech #DigitalAgriculture #FarmersFirst #BuiltForFarmers
