AI is Making Technical Debt Cheaper to Create

The easiest way to misunderstand AI coding tools is to confuse the cost of producing code with the cost of owning software.

Those are not the same number. They have never been the same number. They are becoming more different.

When a model can scaffold a feature, sketch a migration, generate tests, wrap an API, or translate a component pattern in seconds, the production side of software looks dramatically cheaper. A team that used to spend two days getting a rough first version onto the screen may now spend two hours. That feels like acceleration because it is acceleration at one point in the lifecycle.

The trouble starts when that local speed‑up is treated as the whole economic story.

Generated code still enters the same maintenance economy as hand‑written code. It still has to be reviewed, understood, tested, deployed, observed, debugged, documented, refactored, and eventually changed by somebody else who did not write it. If AI lowers the cost of creating code faster than the organisation can absorb the cost of understanding and governing it, then the likely result is not clean productivity. It is faster debt creation.

That is one reason the output‑versus‑outcome distinction matters so much. I covered the measurement side of it in The AI Productivity Mirage. This article is the codebase version of the same problem. AI does not make technical debt irrelevant. It changes the rate at which teams can acquire it.

Writing Code is Cheap. Owning Code is Not

The Software Engineering Institute has long described technical debt as the result of short‑term design or construction choices that increase future complexity and cost. That definition matters because technical debt is not limited to bad syntax or obvious bugs. It includes structural decisions that make future change slower, riskier, and more expensive than it needed to be.

Generative AI lowers the cost of making those decisions quickly.

That can be helpful when a team is exploring unfamiliar APIs, assembling glue code, or drafting routine patterns. It becomes dangerous when speed at the point of authoring outruns the rest of the delivery system. A generated abstraction that nobody fully understands is still an abstraction the team now has to support. A generated service layer with awkward naming and inconsistent boundaries still becomes part of the long‑term estate if it ships.

Recent technical debt research is already moving in this direction. The Evolution of Technical Debt from DevOps to Generative AI describes new forms of debt showing up around data, infrastructure, explainability, and lightweight AI workflows, while noting that real operational practices often lag behind the rhetoric around faster delivery.

That is the right framing. AI‑assisted engineering does not erase the old debt categories. It adds new ways to accumulate them.

Generated Code Still Enters the Maintenance Economy

Every line of code enters a maintenance economy the moment it lands.

Someone has to know why it exists. Someone has to know what can be changed safely. Someone has to know how it behaves under load, with messy data, with unexpected user flows, with future framework updates, or with a regulator asking for a slightly different interpretation of the same business rule.

None of those questions disappear because the first draft arrived quickly.

This is where a lot of AI coding demos become misleading. They showcase the generation event, because that is the impressive bit. That does not show six months of ownership. They miss the half‑remembered helper function that everybody is now slightly afraid to touch. Nor do they show the tests that look comprehensive until a real incident reveals that they only validated happy‑path assumptions. They do not show the subtle drift between three slightly different implementations generated by three different people in three different prompts.

Technical debt is often discussed as if it were mostly a code smell problem. In practice it is also a context problem. The 2025 industrial case study Technical debt is not just technical argues exactly that, showing how debt in large agile organisations is shaped by coordination, communication pathways, expertise mix, and organisational conditions as much as by individual code decisions.

AI increases the risk that teams create code faster than those surrounding conditions can support.

The Review Bottleneck Moves. It Does Not Disappear

AI‑assisted development rarely removes the need for review. More often it changes the nature of review and concentrates more responsibility in it.

Google's Modern Code Review: A Case Study at Google is useful here because it treats code review as more than a gate for catching defects. It is also a mechanism for knowledge sharing, standards enforcement, and social coordination around code changes.

That matters because AI can increase the amount of code passing through the system faster than it increases reviewer capacity. When the number of generated diffs rises, one of two things tends to happen:

• review effort rises and becomes the new bottleneck
• review standards quietly collapse so the queue still moves

Neither outcome is free.

If review effort rises, senior engineers spend more time filtering weak abstractions, catching incorrect assumptions, and rewriting poorly scoped generated work. If review standards collapse, lower‑quality code enters the system and the debt shifts downstream into incidents, regressions, confused onboarding, and future refactors.

There is no magic third option in which the organisation gets dramatically more code, unchanged review effort, and identical long‑term quality without deliberate process changes.

Generated Tests Can Create False Confidence

Test generation is one of the clearest examples of visible productivity drifting away from real assurance.

A generated test suite looks like evidence of discipline. Coverage goes up. Pull requests look more complete. Review comments feel easier to resolve because there is now a test file in the diff.

But a generated test is only useful if it exercises the behaviours that matter, fails for the right reasons, and helps future engineers understand the intended contract of the code. A large pile of shallow tests can increase confidence at exactly the point where caution is more justified.

NIST's GenAI Pilot Code Challenge is instructive because it treats AI‑generated test code as a technical evaluation problem in its own right, not as an assumption of value (see also NIST's GenAI Pilot Code Challenge evaluation plan). If a national measurement programme has to build a dedicated framework to evaluate whether AI can generate tests reliably, that should be a clue that teams should not accept generated tests as proof of quality by default.

Google's research on mutation testing in code review points in the same direction. Please fix this mutant: How do developers resolve mutants surfaced during code review? shows that surfaced test weaknesses lead to a mixed response, with many mutants unresolved for reasons including questionable value, deferred fixes, and false positives (Google's research on mutation testing in code review).

That is a healthier model of testing. Tests are evidence to interpret, not decoration to count.

Legacy Migration Risk Gets Worse When the Code Looks Easy

Legacy work is particularly exposed to the debt accelerator effect because AI makes it easier to underestimate how much of the real problem lives outside the syntax.

If you ask a model to translate routes, convert component syntax, replace deprecated APIs, or scaffold a new data layer, it may produce a lot of code that looks directionally correct. That is often enough to create false momentum. Leadership sees large diffs and assumes the migration is progressing.

The dangerous part is that legacy migrations fail on hidden contracts more than on missing boilerplate. Preview behaviour. Editorial quirks. analytics dependencies. content‑model edge cases. sequencing constraints. route aliases. unspoken business rules. odd exceptions that only surface during launch rehearsal.

That is why articles like How to De‑Risk a CMS Migration Before the Real Migration Starts remain relevant even in an AI‑assisted world (see also Building for Change: Architecture Lessons from Multi‑Phase Replatforms). Migration risk is rarely about whether the team can produce enough replacement code. It is about whether the team understands which behaviours must not drift.

AI can absolutely help with migration mechanics. It can also make a shallow migration look more advanced than it really is.

Framework Drift and Dependency Drift Do Not Become Less Real

Another awkward pattern is quiet drift.

Large language models generate from statistical patterns learned across time, projects, and documentation states. That means they can emit outdated APIs, mixed conventions, inconsistent dependency choices, or patterns that were normal in one version of a framework but clumsy in the version your team is actually running.

Even when the generated code technically works, it may move the codebase away from its local standards. Over time that produces a different kind of debt: the system still runs, but the internal logic of the codebase becomes less coherent.

This is one place where code generation can be worse than no acceleration at all. Hand‑written code tends to inherit the habits of the current team. Generated code can import habits from the statistical average of many other teams.

The NIST Secure Software Development Framework remains relevant here precisely because it treats software quality, security, and maintainability as lifecycle disciplines rather than one‑off checks. The 2024 SSDF Community Profile for Generative AI and Dual‑Use Foundation Models extends that logic into AI‑specific development contexts.

If your engineering process is not strong enough to control drift, AI gives drift a much cheaper delivery channel.

Documentation Debt is Ownership Debt

Another way AI accelerates debt is by widening the gap between what the code does and what the team can explain.

Generated work often lands with just enough local plausibility to ship and too little surrounding explanation to age well. A reviewer may understand the change in the moment. Six months later, another engineer needs to modify it without the prompt history, without the original author nearby, and without clear notes on why the abstraction was introduced in that form rather than another.

This is not a soft process concern. It is part of ownership cost. If a team keeps producing code faster than it can explain interfaces, invariants, expected failure states, and operational assumptions, then the maintenance burden rises even when the codebase still looks tidy on the surface.

That is another reason senior engineers end up acting as debt governors. They are often the last line of defence between a codebase that is merely growing and a codebase that is growing in ways nobody will want to revisit later.

Senior Engineers Become Debt Governors

This is where seniority changes shape.

The marginal value of a senior engineer in an AI‑assisted team is not that they can type faster than everyone else. It is that they can decide what is safe to accelerate, what must remain tightly reviewed, where the boundaries are, and what kind of debt the organisation is quietly taking on.

That governance role already existed. AI makes it explicit.

Senior engineers become the people who decide:

• which patterns are safe to scaffold
• which parts of the system need deeper design review before code exists
• where generated code must be rewritten rather than lightly edited
• what testing evidence is acceptable
• which migration assumptions need human validation
• when speed is buying useful leverage and when it is borrowing against future clarity

Google's work on design reviews is relevant here because it shows that early design scrutiny can reduce approval time and improve flow precisely by structuring judgement before implementation becomes expensive.

That is the real senior contribution in an AI‑heavy environment. Not resisting the tools. Governing where the tools are allowed to create obligations for the rest of the system.

Practical Safeguards for AI‑Assisted Engineering Teams

If you want the leverage without the debt spiral, the controls do not need to be theatrical. They do need to be concrete.

These are the safeguards that matter most:

• Keep generated changes small enough that reviewers can still build a real mental model of them.
• Require explicit intent in pull requests: what the code is changing, what assumptions it makes, and what was generated versus hand‑authored.
• Review architecture before large‑scale generation, especially on migrations and cross‑cutting refactors.
• Track rework and rollback rates on AI‑assisted changes separately for a period rather than assuming all throughput is equal.
• Treat generated tests as draft evidence until a reviewer is satisfied they express the right behaviour.
• Maintain style, dependency, and framework rules in tooling so drift is caught automatically where possible.
• Make ownership explicit. If nobody is prepared to own the code after generation, it probably should not be merged yet.
• Use AI aggressively on scaffolding, exploration, and repetitive transformation, and much more carefully on boundary‑setting code, domain rules, security‑sensitive logic, and long‑lived abstractions.

The broader point is simple. AI works best when the team is clear about which costs it is reducing and which costs remain stubbornly human.

One practical test is whether the team could explain the generated change to a new engineer without reopening the prompt history. If not, the code may already be cheaper to produce than to own.

That is a simple heuristic, but it is a useful one because debt often begins where explanation effort starts to exceed generation effort.

Conclusion

AI coding tools can absolutely make teams faster. They can reduce friction, shorten drafting time, and help experienced engineers move through routine work with less ceremony.

But faster code creation is not the same as cheaper software.

The cost of ownership still sits where it always sat: review, judgement, architecture, testing, observability, maintenance, migration, and future change. If those parts of the system do not get stronger while code generation gets cheaper, the likely outcome is not a productivity breakthrough. It is a technical‑debt accelerator.

That is the trade worth seeing clearly. AI is making debt cheaper to create because it is making implementation cheaper than understanding. The teams that benefit most will be the ones that notice the gap early and govern it before the codebase starts charging interest.