{
  "id": 20031344,
  "title": "Code Is Cheap Now. Judgment Isn't: The Review Bottleneck and the Vanishing Apprenticeship",
  "url": "https://urgent.news/2026/11/14/code-is-cheap-now-judgment-isnt-the-review-bottleneck-and-the",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-11-14T19:15:26.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/karmen_f883f51347e4136a4e/code-is-cheap-now-judgment-isnt-the-review-bottleneck-and-the-vanishing-apprenticeship-1neg"
  },
  "original_language": "en",
  "account": "The pace at which software code can be generated has accelerated significantly with the advent of AI tools. However, the speed at which developers can assess, evaluate, and trust that code has not kept pace. This disconnect between rapid code generation and slower code review processes poses a significant challenge for software development teams.\n\nThe crux of the issue lies in the review bottleneck, where a surge in code output often outpaces the capacity for thorough review. As a result, software delivery can suffer in several ways: long review queues leading to delays, shallower reviews that overlook critical issues, and senior engineers being pulled into constant reviewing duties, leading to burnout. These problems are not visible on standard productivity dashboards that merely track the number of pull requests opened.\n\nTo address this, teams should shift their focus from measuring output (such as the number of tickets closed or lines of code generated) to evaluating outcomes (like change failure rate, time-to-restore, review time, and incident frequency). If AI tools make engineers feel more productive without corresponding improvements in these metrics, the perceived efficiency gain may be misleading.\n\nThe root of the problem also extends to the apprenticeship problem. Experienced engineers often develop their judgment through hands-on experience: writing and debugging mediocre code, learning from performance and concurrency issues, and facing real-world failure scenarios. AI tools could potentially replace these low-hanging learning opportunities, depriving the next generation of engineers of the necessary experience to become proficient reviewers.\n\nA crucial insight is that judgment is not a static skill that can be learned solely through reading. It develops through a cycle of making decisions, observing the results, recognizing discrepancies, and refining one's mental model. Overreliance on polished AI-generated code can hinder this learning process, especially for junior developers who may lack the comparative context to identify flaws in the code.\n\nInstead of bypassing learning, AI tools should be used as aids to enhance the learning loop. Some effective practices include predicting outcomes before generating code, comparing these predictions to the actual results, maintaining a log of AI suggestions that were rejected, and deriving test cases from functional requirements rather than relying solely on the generated code.\n\nLastly, building a portfolio that demonstrates practical experience in operating software remains essential. While it's still valuable to undertake substantial projects, the quality of the learning process should be emphasized over mere completion. By incorporating these practices, developers and teams can better navigate the review bottleneck, foster the development of engineering judgment, and ensure sustainable growth in software development capabilities.",
  "summary": "Writing code has become the cheapest part of software development. Understanding it hasn't. Over the past year, AI assistants have changed the economics of producing code. A working endpoint, a migration script, or a set of unit tests can appear in seconds. But software doesn't ship when code is written. It ships when someone is confident enough to put it in front of real users. That gap between…",
  "key_points": [
    "AI-generated code creation speed outpaces review processes.",
    "Review bottleneck leads to delays, shallow reviews, and engineer burnout.",
    "Teams should shift focus to evaluating outcomes, not just output."
  ],
  "editors_take": null,
  "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."
}