Historically, software development teams have regarded pull requests as mere checkpoints. Developers would write code, open a pull request (PR), undergo review, merge the changes, and proceed to subsequent tasks. The prevailing assumption was that a PR represented a stable and complete piece of work. However, this assumption is increasingly being challenged.

With the widespread adoption of AI-generated code, the nature of PRs is evolving. While code is produced more rapidly, it is often more challenging to review, comprehend, and trust. Viewing PRs solely as open or merged overlooks critical aspects of the process. In this context, the concept of PR maturity becomes increasingly significant.

What PR Maturity Actually Means

• A mature PR is largely stable at the time it is opened.

• An immature PR undergoes significant changes throughout the review process.

This immaturity typically manifests in the following ways:

• A high number of follow-up commits after the PR is opened

• Large changes in scope during review

• Back-and-forth cycles that reshape the implementation

• Review comments that trigger structural changes, not small fixes

If the majority of substantive work on a PR occurs after the review process begins, this does not constitute a genuine review. Instead, it reflects ongoing development conducted publicly.

Why This Was Already a Problem

Even prior to the advent of AI-generated code, immature PRs incurred significant costs.

• They slow reviewers down because the ground keeps shifting.

• They generate unplanned rework that is not reflected in project planning.

• They blur ownership because the final outcome is a group effort rather than a clear implementation.

Most teams recognize this issue but do not systematically track it. Instead, they prioritize metrics such as cycle time or review speed and assume that these indicators are sufficient.

But things aren’t fine, they’re just hidden.

AI Makes This Worse, Not Better

A common misconception among teams is that AI will inherently improve PR quality by accelerating coding. In practice, AI often initially reduces PR maturity.

Why? Because it’s now easy and quick to generate code. Developers are more likely to:

• Open a PR earlier, before fully validating the solution

• Rely on the review phase to "figure things out"

• Iterate using AI inside the PR instead of before it

So instead of fewer rounds of changes, you end up with more changes happening during the PR process. The result is clear: more commits, more rework, and more noise.

But the dangerous part is that traditional metrics don’t clearly show this degradation. Cycle time might stay flat. Throughput might even increase. Meanwhile, the cognitive load on reviewers increases, and the actual quality signal is diluted.

The Illusion of Progress

AI brings a new kind of illusion. You see more code, you see faster PR creation, you see activity.

This creates the illusion of progress. However, if the code remains unstable upon entering review, the effort is merely shifted to a later stage rather than reduced. This redistribution of work complicates tracking and management.

• Rework is hidden inside PR iterations

• Quality issues are discovered late

• Review becomes a bottleneck instead of a checkpoint

Without monitoring PR maturity, the development process may appear productive, while in reality it is characterized by inefficiency and excessive activity.

What High PR Maturity Looks Like

A high-maturity PR is boring, but in a good way.

• The core implementation is already thought through

• Follow-up commits are small and targeted

• Review focuses on edge cases, not rewriting logic

• The discussion is about refinement, not direction

This does not imply the absence of changes, as some modifications are inevitable. Rather, it indicates that the PR is nearly complete when the review process commences.

What Low PR Maturity Signals

Low PR maturity is not solely a developer concern; it reflects broader systemic issues. It typically indicates the presence of one or more of the following conditions:

• Developers are pushing too early to get feedback

• Lack of confidence in the solution before opening the PR

• Over-reliance on review as a development phase

• AI-generated code is being accepted without enough validation

• Weak pre-PR checks or missing local testing

If you ignore these signals, you end up optimizing the wrong thing. You’ll try to "speed up reviews" instead of fixing what’s breaking them.

How to Measure It Without Overcomplicating

• Ratio of commits after PR open vs total commits

• Lines changed after the review starts

• Number of review cycles per PR

• Frequency of major changes triggered by comments

If these numbers are high, your PRs are immature. It’s as simple as that.

What Needs to Change

If you’re serious about improving this, you need to stop pretending the review phase is flexible. It’s not. It’s expensive. A few changes can actually make a difference:

• First, set a higher standard for opening a PR. If the solution isn’t clear yet, don’t open a PR.

• Second, utilize AI primarily for drafting code rather than generating final solutions. If developers lack a comprehensive understanding of the code, this uncertainty will be reflected in the PR.

• Finally, avoid prioritizing speed at the expense of quality. Rapid PRs with low maturity often incur greater costs than slower, more stable submissions.

The Real Point

PR maturity forces you to look at where the actual work is happening.

The introduction of AI is shifting the boundary between development and review. Increasingly, teams are conducting development during the review phase under the misconception that this constitutes efficiency. In reality, this represents a misallocation of effort.

Failure to address this issue results in a system that appears productive but becomes increasingly difficult to manage. The most significant risk is that these challenges may only become apparent once the review process emerges as the primary bottleneck, at which point remediation is considerably more difficult.