I recently read an article around a new term called “slop debt”, and I found it interesting because before understanding slop debt, I think we first need to understand what technical debt actually is.
So, what is technical debt?
At first, it sounds like some finance thing, but no, it’s a pure technical term.
Let me explain it with an example.
Suppose you are building software for a startup. At the beginning, you don’t have concrete requirements and speed matters the most because you need demos, early customers, feedback, etc. You don’t get enough time to plan everything properly, so you take shortcuts.
Suppose you are building authentication for the application and you need OTP verification. You think:
“Why should I use Redis at this stage? We only have a few users. I’ll just store the OTPs in runtime memory and improve it later.”
So you take a shortcut.
And that shortcut becomes technical debt.
It works perfectly at the beginning. But later, when you have multiple instances, load balancing, deployments, or simply need OTPs to survive a restart, that decision starts creating problems.
The debt isn’t that you used memory.
The debt is the future cost created by that decision.
The example I gave is very basic, but there can be multiple instances of technical debt: not properly structuring your code, not splitting modules, choosing an architecture that won’t handle future load, writing a poor database model, skipping tests, hardcoding things, etc.
Every time you think:
“Let’s get it working before we make it right.”
you are potentially borrowing from your future self or future maintainer.
And honestly, that’s not always wrong.
In a startup, you sometimes should take technical debt.
If you are trying to validate an idea, spending two weeks designing a perfect architecture for something that might be thrown away next month is also a bad engineering decision.
The important thing is that someone owns that decision.
Someone knows why the shortcut was taken.
Someone knows what the limitation is.
Someone knows that this part will eventually need to be changed.
And most importantly, the customer doesn’t care how your code works.
They care whether the product works.
Until it breaks.
Then AI entered the picture
With AI agents, building software has become extremely easy.
You can describe a feature, and an agent can create the database model, APIs, services, frontend, tests and sometimes even deployment configuration.
And the problem isn’t that AI can write all of this.
The problem starts when nobody is actually making the architectural decisions anymore.
The AI’s job is simple:
Build the thing.
So it optimizes for exactly that.
Need a new feature?
Create a new model.
Need to store something?
Add another table.
Need another API?
Create another service.
Need authentication?
Add another authentication layer.
Need caching?
Add Redis.
Need another database?
Sure, why not.
Each decision can look completely reasonable when you look at it individually.
But software isn’t a collection of individual decisions.
It’s a system.
And that’s where I recently saw something that made the idea of “slop debt” very real to me.
A project built entirely with AI agents
One of my friends came to me recently and started discussing a project he had built.
It was a task management + CRM platform.
I won’t lie, the product looked awesome.
It had a lot of features, and honestly, there were things in it that I would prefer over some of the standard tools I use.
He told me the project was ready and he wanted to deploy it so he could showcase it to a customer.
He had already acquired the customer as well.
So I deployed it.
And it worked perfectly.
The customer liked it.
Everything looked good.
Until the customer started asking for changes.
Now, my friend isn’t a fully technical person. He had built almost the entire thing using multiple AI agents.
He wanted to hire me part-time to handle the changes.
I agreed and looked at the codebase.
And…
Oh god.
Nothing made sense.
There was no proper documentation.
No useful README.
The structure was inconsistent.
There were abstractions everywhere.
Some parts were over-engineered while other parts were basically hacked together.
I spent almost two hours just trying to understand what was happening.
Then I tried using Copilot to help me understand the codebase.
But even that didn’t help much.
The codebase had so many inconsistencies that understanding one part didn’t necessarily help me understand another.
For example, the application had two database layers.
One was PostgreSQL.
The other was MongoDB.
Now, using both PostgreSQL and MongoDB isn’t inherently wrong.
There are perfectly valid systems where you need both.
But here, there was no clear architectural reason for it.
The same domain models existed in both.
Both were being used with the same data model.
There was no clear source of truth.
There was no obvious reason why one piece of data belonged in PostgreSQL while another belonged in MongoDB.
It looked like different agents had made different decisions while building different parts of the application.
Each decision probably made sense in isolation.
Together, they made no sense.
And that was the real problem.
This is where I started understanding slop debt
In a normal codebase, even a messy one, you can usually see its history.
You can find parts that were carefully designed.
You can find parts that were rushed because of a deadline.
You can find a TODO left six months ago.
You can find a comment explaining why something weird exists.
You can talk to the engineer who made the decision.
There is some history behind the mess.
But in a codebase generated mostly by AI agents, you can end up with something different.
The entire codebase can look equally developed while having no coherent architectural decision behind it.
That’s what I think makes slop debt different.
It’s not simply bad code.
It’s code accumulated through a large number of locally reasonable AI decisions without a human consistently owning the system-level architecture.
And when something breaks, you don’t necessarily know why the system was built that way in the first place.
So how is slop debt different from technical debt?
Technical debt usually has a decision behind it.
Maybe you had a deadline.
Maybe you didn’t have enough engineers.
Maybe the requirements weren’t clear.
Maybe you deliberately chose a simpler implementation because you wanted to validate the idea first.
Someone made a trade-off.
You might regret that decision later, but you can still understand it.
That’s what makes technical debt relatively manageable.
You can look at the code and say:
“Yeah, this was done because we needed to ship quickly. We should replace this with X later.”
You know where the debt came from.
With slop debt, the problem can be much deeper.
You may not even know which decision created the problem.
Was this abstraction created because it was actually needed?
Or did an AI agent create it because it thought it was a good pattern?
Why are we using MongoDB here?
Why is this data duplicated?
Why are there three different service layers?
Why does this API call another API inside the same backend?
Why are there two different authentication mechanisms?
Why does this module work completely differently from the rest of the application?
And sometimes the answer is simply:
Nobody knows.
The dangerous part
The most dangerous thing about slop debt is that the product can work perfectly.
That’s what makes it different.
My friend’s application was working.
The customer liked it.
There were no obvious problems from the outside.
The problem only appeared when someone asked:
“Can we change this?”
That’s when maintainability became important.
A product isn’t finished when it works.
It is finished when you can continue changing it without being afraid of breaking everything else.
And this is where AI-generated systems can become dangerous.
If you use AI to generate code but you still own the architecture, understand the database model, review the important decisions and maintain consistency across the system, there isn’t necessarily a problem.
AI can be an incredibly powerful implementation tool.
But if you let AI make the architecture decisions as well, while you only care about whether the feature works, you are creating a system where the person responsible for maintaining it might not understand why it exists.
You should own the architecture
I think this is the most important thing when building software with AI.
AI can write the code.
But you should own the architecture.
You should know:
- Why are we using this database?
- What is the source of truth?
- Why does this service exist?
- How does authentication work?
- Where does this data live?
- What happens when we have 100x the current traffic?
- What happens when this service fails?
- Why does this module communicate with that module?
- What are the boundaries of the system?
You don’t need to manually write every line of code anymore.
But you need to understand the decisions behind those lines.
Because when the customer asks for the next feature, the AI won’t be the one maintaining your architecture.
You will.
And if you don’t own the architecture, you don’t really own the codebase either.
That’s the difference I see between technical debt and slop debt.
Technical debt is often a shortcut you knowingly or unknowingly take from your future self.
Slop debt is what happens when you let a system accumulate thousands of decisions without anyone truly owning those decisions.
And the worst part?
You might not even realize you’ve taken the debt until someone asks you to change something.