AI Makes New Code Cheap. Refactoring Becomes More Valuable.

AI lowers the cost of producing candidate code.

That sounds like refactoring should become less important. If a team can generate a new endpoint, test scaffold, migration, adapter, or UI state in minutes, why spend time reshaping code that already works?

The opposite is true.

The risk is not that every AI-assisted change is less maintainable. The risk is that when adding another implementation becomes cheap, organizations can accumulate more software surface area than their review and maintenance systems can comfortably own.

That makes refactoring more valuable, not less.

Cheap generation changes the economics

There is credible evidence that AI can reduce code creation cost in some contexts. Microsoft Research's controlled GitHub Copilot experiment found that developers with Copilot completed a specific JavaScript HTTP server task 55.8% faster than the control group. That is not a universal productivity estimate, but it explains why AI-assisted developers often feel the first-draft step getting lighter.

GitHub made a related point in its July 2026 article, "The cost of saying yes has changed." A generated first patch can be useful as a price check: it turns a vague scope debate into a concrete diff that humans can inspect. But GitHub also draws the essential boundary. Cheap to write is not the same as cheap to own.

That boundary is where refactoring becomes strategic.

If code is expensive to produce, teams naturally resist adding it. If code is cheap to produce, teams need a different control mechanism. They need to keep the system easy to change after many cheap additions have accumulated.

Refactoring is one of those mechanisms.

Current evidence is more balanced than the slogans

A 2026 Empirical Software Engineering paper, "Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability," gives a useful caution against easy claims. The preregistered two-phase study involved 151 participants, 95.4% of whom were professional software developers. In Phase 1, the authors observed that AI-assisted participants had 30.7% shorter median completion time, but Phase 1 was observational and not randomized with respect to AI use, so that number should not be treated as a causal RCT result.

The second phase was the downstream maintainability test. In that randomized controlled trial, new participants manually evolved Phase 1 solutions without AI assistance. The authors found no significant difference in subsequent manual evolution completion time or code quality, and did not detect systematic maintainability advantages or disadvantages from AI-assisted development within their tasks and measures.

That finding matters because it prevents the wrong lesson. The lesson is not "AI-generated code is less maintainable." The better lesson is that lower code-creation cost changes the economics of software volume. Even if individual AI-assisted changes are maintainable, organizations still need discipline around consolidation, simplification, refactoring, and code ownership.

The same paper also points to a risk that deserves more research: code bloat from excessive generation. That is exactly where refactoring enters the AI discussion. The concern is what happens when a codebase keeps growing faster than teams can consolidate and understand it.

Refactoring protects the change curve

Martin Fowler describes refactoring as improving the design of existing code through small, behavior-preserving transformations. That definition matters in the AI era because refactoring is not aesthetic cleanup. It is a way to keep future changes cheap.

Technical debt is the related warning. Fowler frames technical debt as internal quality deficiencies that make later modification harder. The "interest" is the extra effort a team pays each time it changes the system through unclear structure, duplicated logic, leaky abstractions, and brittle tests.

AI can accidentally raise that interest rate by making incremental additions feel nearly free.

Not because AI is uniquely bad at code. The reason is simpler: lower creation cost changes behavior. Developers can generate more code before the organization has fully priced its downstream cost. A one-off adapter is easier to accept. A duplicated validation path is easier to tolerate. A test that mirrors implementation details is easier to keep. A large AI-assisted diff can look productive even when it makes the next change harder.

Refactoring converts cheap creation into cheaper future change. Without it, AI can make teams faster at adding surface area and slower at owning the resulting system.

Observational signals are worth watching

A 2026 GitClear/GitKraken observational analysis of 623 million code changes from 2023 to 2026 reports a simultaneous decline in refactoring signals and rise in duplication during the AI-adoption period. The report says refactoring line moves were down 70%, code block duplication was up 81%, and long-term legacy maintenance was down 74%.

That pattern is noteworthy, but it is observational evidence from a commercial provider's longitudinal analysis. It does not establish that AI caused the shift.

The right use of this data is as a warning signal, not causal proof. If code generation gets cheaper while refactoring, reuse, and legacy maintenance signals weaken, leaders should ask whether their organizations are preserving the structural habits that make software affordable to own.

The failure mode is local speed and global drag

A subtler risk is code that works locally but increases global drag:

A generated helper solves today's case but forks a domain concept that already existed somewhere else.
A new service path passes tests but bypasses the team's normal observability or error-handling pattern.
A quick UI state duplicates business rules from the backend because it was faster than finding the shared contract.
A generated test increases coverage while making implementation details harder to change.
An AI-assisted change touches more files than necessary, so reviewers spend time reconstructing intent.

Each change can be defensible on its own. Together, they make the codebase harder to reason about.

This is why DORA's 2025 State of AI-assisted Software Development report is useful. DORA frames AI as an amplifier of the organization's existing strengths and weaknesses, and emphasizes that returns come from the underlying organizational system, not only the tools. AI in a clear, well-tested, modular system can amplify good habits. AI in a tangled system can amplify confusion.

Refactoring is how teams improve the system that AI amplifies.

Refactoring becomes a review strategy

In many teams, refactoring is treated as a separate category of work: important, but easy to postpone when feature pressure rises.

That separation becomes weaker when AI is involved.

If an AI-assisted change is hard to review, the team has a choice. It can spend senior attention understanding a messy diff, or it can refactor the surrounding code so the next diff is smaller and clearer. The second option often has higher leverage.

The review question changes from:

"Is this generated code acceptable?"

to:

"What structure would make the next generated change easier to verify?"

That is a more durable question. It pushes refactoring into the flow of everyday work:

Extract the domain rule before generating another copy of it.
Rename unclear concepts before using AI to extend them.
Split a large module before accepting another conditional branch.
Add characterization tests before using AI to reshape legacy code.
Reduce pull request size so reviewers can validate intent instead of decoding volume.

In this model, refactoring is not a detour from AI productivity. It is infrastructure for AI productivity.

What leaders should measure

Engineering leaders should not measure refactoring by how many cleanup tickets were completed. That recreates the same activity-metric problem AI already exposes.

This is a general engineering scorecard, not a list of GitMe metrics. Teams may need repository, code-quality, CI/CD, review, delivery, and qualitative organizational data to evaluate these signals.

A better scorecard asks whether refactoring reduces the real cost of future work:

Review clarity: are AI-assisted diffs becoming smaller, more localized, and easier to understand?
Rework ratio: do refactored areas require fewer follow-up corrections after review, merge, or production use?
Effort durability: do contributions remain useful through later maintenance, refactoring, and product change?
Duplication pressure: are generated helpers, tests, and workflows consolidating around shared concepts instead of multiplying variants?
Change latency: do similar future changes move through the delivery system with less waiting and less uncertainty?
Test resilience: do tests protect behavior while allowing implementation structure to improve?
Ownership confidence: do maintainers believe the system is becoming easier to extend, operate, and explain?

These signals are harder to fake than "we refactored 5,000 lines." Improving them requires making the system easier to change.

Where GitMe fits

GitMe provides a different downstream engineering view. AI Effort Share helps show where AI-assisted work appears. Real Effort Value helps distinguish meaningful engineering contribution from raw generated volume. Work categorization helps leaders understand the kinds of work flowing through the organization. Rework and historical comparison provide context on downstream correction patterns over time. Effort Survival Cohort provides a cohort-based view of how much past engineering effort remains effective over time.

That matters for refactoring because the value of refactoring often appears later. The immediate diff may not ship a visible feature. The payoff is that future work becomes easier to review, easier to adapt, and less likely to create avoidable rework.

GitMe should not be used to claim that every refactor is valuable by default. Some refactors are premature. Some are too broad. Some hide product risk behind technical language. The useful question is whether the refactor changes the economics of future work.

This connects directly with GitMe's views on the real cost of AI-generated code after the first draft, AI moving the engineering bottleneck from coding to review, measuring whether AI-generated code survives in production, and why AI usage metrics still do not measure AI ROI.

AI makes new code cheap. That is a real advantage.

But cheap code changes the leadership problem. The scarce resource is no longer only implementation time. It is review attention, ownership confidence, architectural clarity, and the ability to keep changing the system without accumulating drag.

Refactoring is how teams protect that ability.

In the AI era, the best engineering organizations will not be the ones that generate the most code. They will be the ones that make good code easier to generate, easier to verify, and easier to keep.