AI-Generated Code Is Faster. Can Your Team Still Review It?

A developer can now generate a working feature before the next standup. Someone still has to read it, check its assumptions, and decide whether it belongs in the codebase. For many teams, review has not kept pace. 

When engineering leaders tell me AI has sped up coding but releases still feel fragile, review is the first place I look. AI-generated code shortens the time it takes to write a change. It doesn’t shorten the time it takes to trust one. 

What Recent Data Shows About AI Code Quality 

Three independent sources, each measuring something different, point in a similar direction. 

• Code structure: In GitClear’s 2026 research, moved code fell from 21% of changed lines in 2022 to 3.8% in 2026 year-to-date. GitClear treats moved code as a signal of refactoring. Over the same period, copy-pasted code rose from 9.4% of changed lines to 15.7%. 

• Delivery stability: Google’s 2025 DORA report surveyed nearly 5,000 technology professionals. It found that AI adoption has a positive relationship with throughput and a negative relationship with delivery stability. 

• Review load: Faros AI’s 2026 telemetry compared periods of low and high AI adoption. Under high adoption, median time in pull request review was 441.5% higher, and average pull request size was 51.3% higher. 

These findings are observational, and none of them proves that AI alone caused the shifts. Together, though, they suggest that code is being produced faster than teams can absorb it. That raises a useful question for leaders: are we adding code faster than we improve what already exists? 

Why AI-Generated Code Slips Through Review 

Reviewers learn to spot human mistakes, such as a rushed shortcut or a missing error case. AI output tends to look polished, and that polish hides its weaknesses. 

Consider a payment call that times out. An AI-generated fix might add an automatic retry that looks correct and passes the tests. But if the retry sends no idempotency key, a request that actually succeeded after a slow response gets sent again, and one purchase could result in two charges. 

When a pull request arrives without context, the reviewer has to reconstruct its intent from the diff alone. That is slow work, however clean the code looks. 

Build a Code Review Process Around Intent 

Before a change reaches review, it should answer three questions: 

1. What behavior should change? State the expected outcome and at least one important failure case, such as that timeout, before generating any code. 
2. Why does it belong here? Name the component, interface, or convention the change should follow. 
3. What evidence supports merging? Link the relevant tests and security checks, and flag anything that still needs manual inspection. 

Scale scrutiny to risk. A copy change and a payment-flow change shouldn’t get the same review. For high-risk work, review the design and acceptance criteria before any code exists. That discipline keeps custom software maintainable after the first release. 

Measure Where Review Time Actually Goes 

Limiting pull requests by line count alone won’t fix a growing queue. Smaller pull requests help only when each one can be understood and tested on its own. 

Track these four measures for a few weeks. Treat each one as a prompt to investigate, not a verdict: 

• Time to first review. A long wait may point to limited reviewer capacity. 

• Time from first review to merge. Repeated back-and-forth can reflect unclear requirements. 

• Rework shortly after merge. Early corrections can expose weak validation. 

• New duplicated code. Rising duplication can signal that reuse is breaking down. 

Read together, these measures show where to look first, so you can adjust your DevOps and delivery practices where the delay actually occurs. 

Speed Should Survive the Merge 

AI-generated code earns its value when engineers can explain, test, and support what it produces. If output rises while context, validation, and ownership stay thin, today’s speed can become next year’s maintenance work. 

Before celebrating a faster sprint, ask one question: could someone who didn’t generate this code safely change it six months from now? 

If the answer is uncertain, you’ve found your next engineering priority. Talk to Pumex about building a delivery process that keeps your software understandable as it scales.