Quoting Kenton Varda

2026-07-08 · Source: Simon Willison's Weblog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

On July 8, 2026, Kenton Varda implemented a team-wide moratorium against using AI for generating change descriptions, specifically targeting pull request (PR) and commit messages, alongside issues and tickets. Varda explained that these AI-written descriptions were "worse than useless" during his code review process. The core problem identified was the AI's tendency to detail low-level code specifics, information readily available by directly inspecting the code. Critically, the AI consistently omitted the necessary higher-level framing required to understand the overarching intent and broader context of the code modifications, thereby impeding efficient and meaningful reviews.

Key takeaway

For engineering leads or AI/ML directors evaluating AI tools for developer productivity, you should critically assess the quality of AI-generated documentation. Your teams risk reduced review efficiency if AI-written change descriptions lack high-level context, forcing reviewers to manually infer the "why." Consider implementing strict guidelines or temporary moratoriums on AI-generated commit messages and PR descriptions until these tools reliably provide strategic framing over mere code reiteration.

Key insights

AI-generated change descriptions often lack high-level context, making them unhelpful for code review.

Principles

• Contextual understanding is paramount for effective documentation.
• AI output needs human oversight for critical framing.
• Low-level details are redundant if visible in code.

In practice

• Review AI-generated text for high-level context.
• Prioritize human-written summaries for complex changes.
• Define clear guidelines for AI documentation tools.

Topics

• AI Documentation
• Change Descriptions
• Code Review
• Developer Productivity
• Software Engineering

Best for: CTO, VP of Engineering/Data, AI Product Manager, Software Engineer, Director of AI/ML, AI Engineer

Related on AIssential

• AI Can Generate Code in Minutes. Reviewing It Responsibly Is the Real Engineering Skill — AI accelerates code generation, but shifts the engineering bottleneck to responsible review and validation.
• The AI Labor Shift: Why We Are Transitioning from ‘Doers’ to ‘Reviewers’ — Generative AI shifts labor from task execution to human review and system orchestration, demanding contextual judgment.
• Linus Torvalds on the AI claim that makes him angry, and what security researchers should never do — AI tools boost developer productivity and contribution volume but introduce social and security challenges in open-source projects.
• AI isn’t making developers more productive – it’s making them busier — AI coding tools increase raw code output, but human-centric downstream processes become the new bottleneck.
• Organizational Context for AI Coding Agents with Dennis Pilarinos — Bridging the organizational context gap is crucial for effective AI coding agents and human developers.
• The Hidden Bill Behind the AI Boom: While saying we are speeding up, are we mortgaging the future? — AI assistants often defer technical debt and degrade software architecture, shifting developer toil rather than eliminating it.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.