The abandoned PR page

By Bulat at aictrl.dev, co-authored with AI. This is a condensed version of our original research, published on 15 October 2026. The original includes interactive charts and methodology.

In the third quarter of 2026, openclaw merged about 2,700 pull requests a week. 88% of them merged without a human comment from anyone other than their author.

The pull request is still how code gets into these projects. But the page around it, where people used to question a change before it landed, has gone quiet.

That is the far end of a pattern we found in 26 popular open-source repositories. Projects that started in the AI era merge far more pull requests and talk about each one far less. Established projects haven't sped up much, but they are moving the same way: fewer human comments per pull request, bigger pull requests, and more of the conversation written by bots.

We count comments, not attention. Silence on GitHub doesn't prove nobody read the change. It does mean the page no longer carries the review.

What we measured

We counted merged pull requests from January 2024 to September 2026, 240,653 in total before excluding dependency and release automation. For comments, size, and approvals, we drew a random sample of up to 100 merged pull requests per week, 143,815 in total, weighted by each week's merge count. Excluding codex's weeks from mid-May 2026 leaves 143,297 sampled pull requests.

We compared two groups:

• Established repositories (16): react, next.js, vscode, cpython, django, rust, kubernetes, svelte, vite, astro, tailwindcss, deno, bun, mermaid, playwright, and xyflow.
• AI-native repositories (10): opencode, pi, codex, gemini-cli, openclaw, crush, qwen-code, kilocode, cline, and goose.

For each repository and quarter, we tracked merges per week and human comments per merged PR. Human comments include conversation comments, inline review comments, and review summaries from people other than the author. Bot commands and repeated templated messages do not count. Bot comments are tracked separately as AI review or other automation.

The baseline is Q1 2025. This was already a world with AI coding tools; it is a comparison point, not a pre-AI control group. The established repositories' median human comments per PR stayed between 1.8 and 2.1 in every quarter of 2024.

Established projects: bigger PRs, less conversation

Between Q1 2025 and Q3 2026, the median established repository changed as follows:

These are medians across repositories, not totals weighted by pull request volume.

The median PR got about 90% bigger and received about 40% fewer human comments. Throughput barely moved. Twelve of the 16 established repositories now receive fewer human comments per pull request than in early 2025.

Count bots along with people and review looks busier: the average established repository went from 4.3 comments per merged PR to 6.0. About half the added bot comments are automation such as CI status, reports, previews, and checks. The other half is AI review, concentrated in a few repositories. More comments do not necessarily mean more discussion between maintainers.

AI-native projects: volume changes the picture

The median AI-native repository merged 76 pull requests a week in Q3 2026, compared with 32 for the median established repository. The average is much more lopsided, 392 against 78, because openclaw alone merged about 2,700 per week.

Weighted by PR volume, 83% of AI-native pull requests merged without a human comment in Q3 2026, up from 50% a year earlier. In the median AI-native repository, the figure is 58%, close to the established median of 54%.

The distinction matters: openclaw accounts for three quarters of AI-native volume in Q3 2026. Without it, the volume-weighted figure ends at 69%, not 83%.

Being AI-native does not force a project to skip review. qwen-code merged 268 pull requests a week with 1.3 human comments per PR, and 56% of its pull requests were approved by someone other than the author. It also received 3.9 AI review comments per PR, most posted from maintainers' own accounts.

Where the measurement can mislead

• Discussion can happen elsewhere. We measure what is visible on GitHub. Internal review, chat, and pair programming are outside this dataset.
• Approvals mean GitHub reviews in the approved state. Projects that approve through bot commands, such as Rust's bors or Kubernetes' Prow, can score low on that measure.
• Bot classification is imperfect. Some automated accounts are registered as ordinary people. We classified large bot-like authors and checked unclear cases, but AI-assisted text without markers can remain.
• The codex workflow changed. GitHub merges nearly stopped in mid-May 2026. Later changes were synchronized from an internal repository, where review was out of view. We use codex's Q1 2026 numbers in the comparison and exclude its weeks from mid-May onward.
• This is observational research. Timing alongside model and agent releases does not establish that those releases caused the changes.

What to take back to your team

An AI reviewer can identify possible bugs. That does not automatically leave a person who understands the whole change.

When discussion gets thinner, the PR description becomes more important as a record for the next maintainer. Explain why the change exists, what behavior changes, and where someone should look to verify it.

Decide which changes need a named human sign-off. Django, Rust, and Kubernetes kept the most human review in our sample, and all three require one to merge. That observation is useful context, not proof that the rule alone produces better outcomes.

Finally, measure your own two numbers: merges per week and human comments per PR. If the first rises while the second falls, ask where review and shared understanding are happening now.

Collection date: 15 October 2026; merged PRs from 1 January 2024 to 27 September 2026. Weeks run Monday to Sunday.