What Happens to Technical Debt When Code Is Almost Free to Write?

Technical debt used to have a fairly simple cause. You were under time pressure, so you skipped the tidy version and shipped the quick version, meaning to go back later. Most people never went back. That was the deal everyone understood.

AI has changed the economics of writing code, and I don't think enough people have thought through what that does to debt. When code is nearly free to produce, the old cause of debt (time pressure) mostly disappears. But the debt itself doesn't. It just starts building up for a different reason.

The old model doesn't quite apply anymore

The classic technical debt story goes: a developer knows the right way to do something, doesn't have time, does the quick version instead, and a note gets left in a ticket that says "fix this properly later." Interest accrues. Eventually someone pays it down or the system collapses under it.

That model assumes scarcity. Writing code took effort, so you wrote as little of it as you could get away with, and every corner you cut was a deliberate trade-off someone could, in theory, remember and reverse.

Now a founder or an agency can generate a working feature, an internal tool, or a whole prototype in an afternoon, often without writing a line of it themselves. The scarcity that used to force restraint is gone. You don't need to justify adding another script, another automation, another admin page. It's cheap, so you just add it.

Debt from volume, not corners cut

This is the part I think gets missed. The debt that's building up in a lot of AI-assisted projects isn't really about quality of the code line by line. Generated code is often perfectly readable. The debt is coming from volume and lack of curation.

• Duplicated logic. The same business rule (how a discount is calculated, how a booking gets confirmed) gets written slightly differently in three different places because three different prompts produced it on three different days.
• No single mental model. When one person designs a system, even a messy one, they usually know where everything is. When a system grows through dozens of separate AI-assisted sessions, nobody, including the person who commissioned it, has a full picture of how it fits together.
• Schema drift. Database tables and fields accumulate because it was easier to add a new column than to go back and understand the old one.
• Dependency sprawl. Every new feature can pull in another library or another third-party API, because reaching for a package is faster than writing something from scratch. Nobody's tracking how many moving parts a "simple" process now depends on.

None of this looks like debt at first. It looks like progress, because you keep shipping things. The bill doesn't arrive until you try to change something and discover that three other things quietly depended on it staying the same.

"Just regenerate it" doesn't clear the debt

A common instinct is: if the code is cheap to write, it's cheap to rewrite, so debt doesn't really matter anymore. You can just ask the AI to redo it properly when it becomes a problem.

That's true for a single file or a small feature. It's not true for a system. The expensive part was never typing out the code. It's understanding what the system needs to keep doing while you change it: which edge cases matter, which workaround exists because a client asked for it eighteen months ago, which "temporary" table three reports now quietly rely on. Regenerating the code doesn't regenerate that knowledge. If nobody wrote it down and nobody who built it is still around to explain it, you're rebuilding blind, just faster than before.

Where this actually bites

I see this most often with businesses that started with a fast AI-built prototype and kept extending it as it grew, rather than pausing to check what was underneath. It also shows up in agencies handing overflow work between different freelancers or AI tools without anyone owning the whole picture end to end.

The moment it becomes a real problem is usually when something needs to scale, integrate with a new system, or survive an audit. Suddenly the questions aren't "does this feature work" but "why are there four ways of calculating VAT in this codebase" and "why does deleting a customer sometimes leave orphaned records and sometimes not." Those are exactly the kind of things I get asked to untangle when I take on legacy software modernisation work, and increasingly the "legacy" system in question is only a year or two old.

What actually helps

You can't stop technical debt from happening, cheap code or not. But you can stop it from compounding invisibly:

• Have one person who genuinely understands the whole system, not just the last feature added to it. Someone who can answer "what depends on this" before you change it, not after it breaks.
• Do a periodic pass to consolidate duplicated logic, before you have five versions of the same rule instead of two.
• Before you scale, integrate, or take on paying customers, get someone experienced to look underneath the surface, the same review I'd recommend before taking an AI-built prototype into production.

Cheap code was always going to change how much software gets built. It hasn't changed what it costs to keep that software understandable. That part still takes a person paying proper attention, and it's still worth budgeting for.