{
  "id": 6381505,
  "title": "The AI Code Review Bottleneck: Why Our Merge Time Tripled",
  "url": "https://urgent.news/2026/09/09/the-ai-code-review-bottleneck-why-our-merge-time-tripled",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-09T04:38:59.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/ji_ai/the-ai-code-review-bottleneck-why-our-merge-time-tripled-5444"
  },
  "original_language": "en",
  "account": "The AI code review bottleneck refers to the gap between how quickly code can be generated and how slowly it can be approved. As AI-generated code became faster and cheaper to produce, human attention remained unchanged, leading to longer queues and slower merges. This bottleneck was exacerbated by larger PR sizes and a lack of effective review strategies. The bottleneck arose due to three main factors: a doubling of PR arrival rates, the increasing size of PRs, and superlinear review effort with growing diff size. AI-generated code proved particularly challenging to review due to its uniform plausibility, lack of intent, and redundant code. Responding to these issues, the author implemented a series of measures, including a hard diff-size cap, a \"what I actually verified\" section in PR templates, and an \"explain-back\" rule requiring reviewers to understand the code in one sentence. These changes significantly improved review efficiency and reduced the number of PRs stuck in the queue.",
  "summary": "The pull request was 1,140 lines. The description was four bullet points. It had been opened eleven minutes after the ticket was assigned. I looked at it for forty minutes, approved it, and merged something I did not fully understand. That was the week I started keeping numbers, and the numbers showed an AI code review bottleneck sitting in the middle of my team like a parked truck. We were…",
  "key_points": [
    "AI code review bottleneck caused merge time to triple",
    "Three main factors: doubled PR rates, larger PR sizes, superlinear review effort",
    "Implemented measures: diff-size cap, verification section, explain-back rule"
  ],
  "editors_take": "Implementing targeted review strategies and templates has helped alleviate the AI code review bottleneck, allowing for more efficient processing of code and reducing queue congestion.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}